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Journal of Digital Imaging logoLink to Journal of Digital Imaging
. 2017 Oct 24;31(2):262–274. doi: 10.1007/s10278-017-0032-0

An Efficient Pipeline for Abdomen Segmentation in CT Images

Hasan Koyuncu 1,, Rahime Ceylan 1, Mesut Sivri 2, Hasan Erdogan 3
PMCID: PMC5873470  PMID: 29067570

Abstract

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient’s diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline’s optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

Keywords: Abdomen segmentation, Edge detection, Computed tomography, Statistical pipeline, Image registration

Introduction

Computed tomography (CT) has a wide range of uses in medicine. As a type of CT, abdominal CT scans are utilized to find abnormalities around or inside of abdominal organs such as the liver, spleen, colon, pancreas, kidney, adrenal, and stomach. In this area, valuable solutions and hybrid structures are designed using effective algorithms for segmentation, feature extraction, and classification tasks.

In medical image analysis, abdominal CT scans can contain several disadvantages during abdomen-based processes; these disadvantages can be defined as insufficient edge information, effect of the bed section, writing in the scan, low contrast, narrow histogram, and proximity between the edges of the abdomen and the CT. Herein, the most important one at the forefront is the low-quality edges that can preclude accurate segmentation. The second most important handicap is the bed section, which can affect the results of segmentation, feature extraction, and classification. During these operations, other disadvantages (low contrast, narrow histogram, and writing in the CT scan) can also change the ability of a computer-aided diagnosis (CAD) system in terms of integrability.

Abdomen segmentation is defined with various different names in literature. Koss et al. [1] realized the segmentation of abdomen parts including skin segmentation, which specifies the outer edge of the abdomen. In their study, a segmentation process surrounding the outer edge of the abdomen was not directly identified, but the skin part including the colon was segmented with 91% accuracy. Atlas-based procedures were proposed to segment the different parts of the abdomen [25]. However, neither the contours of the abdomen nor the direct obtainment of the abdomen atlas was realized to prepare the data for further stages. Rangayyan et al. [6] realized the landmarking of abdomen, but the abdomen part was so brilliant in their study that it could be obtained with a simple binarization process. Xu et al. [7] designed the augmented active shape models (AASM) for segmentation of the abdomen and spinal cord. According to the visual results in [7], there were no low-quality edged abdominal images or bed section in the CT scans. As seen in the literature, very few studies touch upon the subject of abdomen segmentation, whether directly or indirectly. Besides, no detailed pipeline or technique is proposed to obtain the abdomen from CT images for different situations, which can cause information loss in tumor- or organ-based CAD applications.

The rest of the paper is organized as follows: Section 2 includes comprehensive explanations of the methods and proposed pipeline. Parameter settings are presented with their reasons in Section 3, and the experiments are realized using well-known statistical metrics for evaluating the performance of a pipeline. The necessities of pipeline operators are examined regarding whether the operators are essential. A visual representation is settled to prove the pipeline’s efficiency. Besides, the pipeline performance is examined on training, test, and validation datasets. Finally, Section 4 contains concluding remarks.

Methods

The basic aim of this study is to obtain the abdomen for both low-quality and qualified edged abdomen images. This requires forming morphological and spatial transforms and the pipeline should be registered as unaffected by the disadvantages of the nature of CT scans. Concerning this, several efficient approaches (both formed by us and taken from the literature) are used to design a robust pipeline. Herein, the pipeline’s name (AbSeg) is defined using the abbreviation Abdomen Segmentation process.

  1. Largest object finder

  2. Image dilation

  3. Median filter

  4. Gradient magnitude

  5. Contrast-limited adaptive histogram equalization (CLAHE)

  6. Spatial fuzzy C-means clustering

  7. Morphological closing

  8. Zerocross eliminator

In this section, the algorithms used are defined in four subparts: (1) well-known morphological algorithms, (2) proposed simple morphological approaches, (3) SFCM, (4) proposed pipeline. Herein, the techniques are presented in an obvious and brief manner to prevent monotony and provide a better understanding.

Well-Known Morphological Algorithm

Image Dilation

Dilation signifies a morphological process that concretizes the edges and other elements of objects in an image. It enlarges (thickens) objects according to the filter (structuring element) [8]. These elements can be chosen as disk, line (with a different angle), diamond, etc. On grayscale images, dilation is defined as in Eq. (1) [8]. In Eq. (1), A and B are respectively the original image and structuring element, D B symbolizes the domain of structuring element B, and A(x,y) is presumed to be − ∞ outside the image’s domain.

ABxy=maxAxxyy+BxyxyDB 1

Morphological Closing

Closing is a morphological process such as dilation that connects the gray values of objects in an image. In the morphological closing of grayscale images, the connection process is used to smooth the edges of objects. However, it can fill smaller areas than a structuring element and thus it is susceptible to high noise levels. As in the dilation process, closing uses the structuring elements disk, line (with different angle), diamond, etc. On grayscale images, closing is defined as in Eq. (2) [8]. In Eq. (2), closing (A • B) means the combination of dilation process (A ⊕ B) and the erosion of B (θ B).

AB=ABθB 2

Median Filter

The median filter constitutes a nonlinear method that performs a smoothing/denoising process for objects in images [9]. During this process, the neighborhoods of pixels are considered to perform smoothing [9]. Herein, the new intensity of a pixel is defined as the median of its neighborhoods.

Gradient Magnitude

Gradient means the generalized version of the derivative and stands for the direction of the largest increase of function [10]. The gradient magnitude (GM) refers to the slope of the graph in the direction that owns the largest increase [10]. GM is generally utilized to determine the direction of objects in an image, and this feature brings GM to the forefront for applications based on edge detection.

Contrast-Limited Adaptive Histogram Equalization

Adaptive histogram equalization (AHE) improves the irregular contrast distribution by transforming each pixel with a function derived from a neighborhood region. In its basic form, the transformation function is chosen as the histogram of a square surrounding the pixel [11]. Contrast-limited adaptive histogram equalization constitutes an improved contrast enhancement technique using histogram features, and it differs from the general Histogram Equalization with contrast limiting process [11]. At this point, Contrast Limiting is utilized to prevent the amplification of the noise level that arises from the contrast level increase [11].

The Proposed Simple Morphological Approaches

In this study, the two simple morphological approaches we formed were processed to ease the segmentation of the abdomen and improve the abdomen segmentation performance.

Largest Object Finder

Largest object finder (LOF) reveals the largest volume by utilizing the summation of rows and columns. After summation, the largest grayscale changes are found in the vertical and horizontal vectors, and the rectangular volume including the abdomen is obtained by utilizing the coordinates of these largest gray values. Two handicaps are eliminated through the LOF approach: (1) the unnecessary parts belonging to an irrelevant region and (2) the rows and columns including zero values. Herein, the unnecessary parts stand for the areas including the appearance of the bed section and the writings in the scan.

The pseudocode of LOF is presented in Table 1. The first process of LOF is contrast enhancement using logarithmic nonlinear stretching, as shown in Eq. (3) [8].

g=blogaf+1 3
Table 1.

Pseudocode of the LOF algorithm

Pseudocode of LOF
Contrast enhancement with nonlinear stretching (logarithmic transformation—Eq. (3))
Find the summation of an image in the vertical and horizontal directions (obtaining two vectors)
For i = 1 to the length of the horizontal vector
Count the nonzero values and save the coordinates until the end of the vector
End
For i = 1 to the length of the vertical vector
Count the nonzero values and save the coordinates until the end of the vector
End
Calculate the largest lengths in both the horizontal and vertical vectors
Find the largest volume using the coordinates of the largest lengths
Assign zero to the unnecessary parts that are outside the largest volume

In Eq. (3), g and f are respectively the contrast enhanced and original images, a and b are constants adjusting the contrast level.

LOF is used to prevent the negative effects of bed section and the writings that can influence edge detection for the abdomen. In addition, the LOF algorithm can effectively remove the bed section and the writings that do not intersect with the abdomen in the vertical and horizontal directions. However, the handicap of LOF is revealed as the adjacent segmentation of the abdomen and bed section as one part if the appearance of the bed section surrounds the bottom of the abdomen; this situation is shown in Fig. 1.

Fig. 1.

Fig. 1

Vector definition of LOF in a challenging CT image

In Fig. 1, the area between the red arrows (A3) is cleaned, but other parts (bed sections A2 and A1) cannot be removed from the CT scan because the horizontal vector obtained at the bottom of the image and the down sight of the vertical vector include a continuous gray change. Concerning this, the LOF algorithm can only clean A3 because of the (close) surrounding bed section.

In other parts of the pipeline, we try to eliminate this handicap for the obtainment of the abdomen. In addition, LOF is added to the pipeline to guarantee the segmentation of the abdomen with the other efficient parts that are also prepared to minimize the effect of the aforementioned handicaps. Some of the disadvantages can be eliminated at this point, or the necessary operators can prevent the disadvantages influencing the segmentation results.

Zerocross Eliminator

The zerocross eliminator (ZE) fulfills the cleaning of rows and columns in which all intensities equal zero; for this, it uses the summation of the rows and columns. After the summation process, ZE moves forward to the gray values from the middle of the outer edges of the image in four directions. Thus, it eliminates the zero values that came from the rest of the pipeline, meaning that ZE removes unnecessary zeros, sensitively; this situation is presented in Fig. 2.

Fig. 2.

Fig. 2

Elimination of zeros by the ZE algorithm

Figure 2 shows that the ZE algorithm eliminates the zeros in the direction shown with a red arrow, since the other parts include nonzero intensity levels. The pseudocode of the ZE algorithm is as shown in Table 2.

Table 2.

Pseudocode of the ZE algorithm

ZE pseudocode
Find the summation of the image in the vertical and horizontal directions (obtain two vectors)
For i = 1 to the length of the horizontal vector
Count zeros from the left side of the image and save the coordinates until a gray value is met
End
For i = 1 to the length of the horizontal vector
Count zeros from the right side of the image and save the coordinates until a gray value is met
End
For i = 1 to the length of the vertical vector
Count zeros from the up side of the image and save the coordinates until a gray value is met
End
For i = 1 to the length of the vertical vector
Count zeros from the down side of the image and save the coordinates until a gray value is met
End
Find the last coordinates that are the closest points to the edges using the saved coordinates
Obtain nonzero output using these last coordinates

Spatial Fuzzy C-means Clustering

A fuzzy C-means (FCM) algorithm constitutes a clustering method that divides the data into one or more classes [12, 13]. FCM constitutes an iterative structure that obtains partitions by minimizing the objective in Eq. (4) [12, 13].

J=j=1Ni=1cuijmxjvi2 4

In Eq. (4) [12, 13], u ij symbolizes the membership of x j belonging to the i th cluster, c is the number of clusters, v i is the center of the i th cluster, and m is a constant parameter. Herein, the minimization of the cost function is obtained when the pixels close to the center of cluster are handled.

In FCM, u ij and v i are respectively updated according to Eqs. (5) and (6) [12].

uij=1k=1cxjvixjvk2m1 5
vi=j=1Nuijmxjj=1Nuijm 6

Spatial FCM (SFCM) is inspired using the spatial information beyond the neighbor pixels, since these pixels involve similar features. For this, the spatial function should be defined as in Eq. (7) [12].

hij=kHxjuik 7

In Eq. (7), H(x j) is a square window on pixel x j, h ij stands for the probability of whether x j belongs to the i th cluster. Herein, the value of h ij is higher if most of its neighbor pixels pertain to the same cluster with x j. Spatial information is added to general FCM as in Eq. (8) [12].

uij=uijphijqk=1cukjphkjq 8

In Eq. (8), p and q are control parameters and the whole process prevents the noise affecting the clustering results by utilizing the neighbor intensities and weights [12].

Proposed Pipeline (AbSeg)

AbSeg is an efficient pipeline that can be used on low-quality and qualified edged abdomen images. The proposed pipeline is not affected by the appearance of the bed section close to the bottom of the abdomen. The small writing at the corner of the images is also removed by wise additions (operators) in pipeline. Besides, AbSeg can be used on images that include low contrast and on scans owing to a narrow histogram. The flowchart of the pipeline is as shown in Fig. 3.

Fig. 3.

Fig. 3

Flowchart of AbSeg

As seen in Fig. 3:

  • The largest volume is obtained for the first part of the pipeline using the LOF algorithm, and the unnecessary parts (appearance of the bed section in CT, and writings above and below the abdomen) are removed. Herein, it should be defined that the bed section and the writings can be removed by simple thresholding techniques. However, this situation can lead to distortion on high-intensity pixels inside organs and other tissues. In AbSeg, the LOF algorithm is utilized to obtain the largest area (abdomen) without any loss for intensity values.

  • The LOF algorithm involves two handicaps on some CT scans: (1) the bed appearance surrounding the abdomen and (2) the sensitivity to the noise that occurs at the edge sights of the CT scan. Therefore, other parts of AbSeg stay necessary for the optimal segmentation of the whole abdomen. Herein, every part is used to eliminate one or more handicaps that occur in the CT scan.

  • In the second part, image dilation is realized to obtain the outer edges of the abdomen for low-quality (discontinuous) edges. For this, image dilation is performed using the line type-structuring element, and 36 different directions (0°, 5°, …, 175°) are used to concretize the abdomen’s outer edge.

  • In the third part, a median filter is used to smooth the thickened edges obtained through image dilation. In addition, it reduces the blurry vision around the edges.

  • In the fourth part, the gradient magnitude is obtained to discover the outer edges of the abdomen.

  • In the fifth part, morphological closing is fulfilled to combine the different intensity (grayscale) values inside the abdomen.

  • Afterwards, CLAHE is processed to improve the contrast features of the image (output of morphological closing) in case the CT’s contrast level seems poor.

  • After CLAHE, the SFCM algorithm is run to segment the abdomen part from the background. In the output of SFCM, a binary image is ensued that includes gaps inside the abdomen. Herein, the filling process is implemented to fill the connected components.

  • Thereafter, the coordinates of the rectangle enclosing the abdomen are detected using the largest filled region in the binary image.

  • In the 10th part, ZE is operated to remove the rows and columns in which all intensities equal zero. In the last part, the abdomen image is reorganized by the scaling process (550 × 700).

As seen in AbSeg, necessary operators are processed to guarantee the segmentation of the abdomen on different characterized abdominal CT images. Parameter settings and experiments are presented in the next section.

Results and Discussion

This section first presents the parameter settings and problems, and then the experiments are investigated and interpreted in detail. For this, trials were implemented in Matlab R2015b on a personal computer with 2.60 GHz CPU and 6 GB RAM running Windows 8.

For performance evaluation, the used data was divided into three groups: training (16 images), test (16 images), and validation (26 images). Training and test images were provided by Siemens Somatom Definition Flash CT. In this device, the reconstruction package is advanced modeled iterative reconstruction (ADMIRE) and used the industry’s first raw-data-based iterative reconstruction method (SAFIRE). Validation data was taken from The Cancer Imaging Archive (TCIA) [14, 15]. In the validation dataset, chest and pelvic CT images were added to the trials beside the abdominal CT images.

Sensitivity, specificity, precision, classification accuracy (CA), dice, and jaccard were used as performance metrics for a detailed examination [1623]. In Eq. (914), TP, TN, FP, and FN, respectively, symbolize the number of true-positive, true-negative, false-positive, and false-negative results.

Sensitivity=TP/TP+FN 9
Specificity=TN/TN+FP 10
Precision=TP/TP+FP 11
CA=TP+TNTP+TN+FP+FN 12
Dice=2TP/2TP+FP+FN 13
Jaccard=TP/FN+FP+TP 14

Parameter Settings of AbSeg

AbSeg includes coherent operators that can regulate one or more problem. At this point, AbSeg’s parameter settings play an important role in the elimination of handicaps for accurate abdomen segmentation. In other words, parameter settings are performed according to the problems based on the abdomen’s features (shape, intensity, structure, etc.). Some of these are as shown in Figs. 4 and 5.

Fig. 4.

Fig. 4

Problems and accomplishments for closing

Fig. 5.

Fig. 5

Dilation problem and accomplishment

Figure 4 a and b show an abdomen image close to the edges of the scan. Figure 4 a shows the closing process attaching the abdomen to the edges of the CT scan in which the structuring element is chosen as disk. Figure 4 b presents the closing that includes two line-type structuring elements that combine the results with angles 15 and 165° to prevent adhesion around the right and left edges of the abdomen. As seen in Fig. 4 a and b, it is explicit that the structuring element should be properly chosen for abdomen images in which the outer edges of the abdomen stay close to the edges of the CT scan.

Figure 4 c and d handle the images that include the appearance of the bed section close to the bottom of the abdomen. Figure 4 c shows the results with disk type closing, while Fig. 4 d presents the closing results obtained by two line-type structuring elements combining the results with angles 15 and 165°. Figure 4 c and d reveal that if the structuring element is not correctly chosen, the gap between the bed section and the abdomen can cause information loss for abdomen segmentation.

As seen in Fig. 4, the type of structuring element plays a key role in eliminating the negative effects of close CT edges and close bed appearance to the abdomen. Besides, the other types of structuring elements (diamond, square, etc.) ruin the obtainment of the abdomen. Experiments have shown that the line-type structuring element performs a better closing process because it uses two opposite angles (15 and 165°) that are the optimum angles for abdomen detection.

As seen in Fig. 5, the outer edges of the abdomen involve unconnected parts and should be linked together. In Fig. 5 a and b, the dilation process is realized to reveal the outer edges of the abdomen. Figure 5 a presents dilation using the disk-type structuring element, but this process cannot reveal the abdomen’s unconnected edges. Figure 5 b shows dilation using the line-type structuring element. The output image is obtained by summing the outputs of line-type dilated images using 36 different angles (0°, 5°,…, 175°) that strengthen the outer edges of the abdomen and complete the unconnected parts. Herein, suitable types of structuring element constitute an indispensable need for the implementation of edge detection.

The constants in LOF (a and b) are adjusted to obtain contrast enhanced-distinct edges and to form a clear input to the LOF algorithm. For the size of median filter, optimum kernel is revealed as [5 5], since smaller values cannot remove noisy and blurry vision. In addition, the larger size can cause the loss of edge information. The SFCM threshold number is chosen as “1” and the proposed pipeline is designed according to this rule.

As seen in the explanations stated above, the optimum parameter values are investigated and ensued according to the abdomen segmentation requirements. Similarly, all operators and the used parameters of operators are examined to achieve the optimal parameter values and operation conditions. Thus, the parameter settings of AbSeg are as shown in Table 3. Herein, these settings are performed by using the training dataset (16 abdominal CT images).

Table 3.

Optimum parameter values

AbSeg section Parameter name Optimum value or range
LOF Constants (a and b in Eq.(3)) a = 10, b = 1
Image dilation Length of lines 3
Angle of lines 0°, 5°,…, 175°
Median filter Size [5 5]
Image closing Length of lines 23
Angle of lines 15°, 165°
SFCM Threshold number 1 (two classes)

Experimental Results

In experiments, 58 CT images were utilized to evaluate the performance of AbSeg. These images include some parts of the following features: low-quality abdomen edges, appearance of bed section and writings, low contrast, narrow histogram, and close abdomen edges to the edges of the scans. In performance evaluation, the results are obtained for the segmentation of the rectangle (ROI) surrounding the abdomen.

In the first part of the experiments, the parameter settings and training performance were obtained using the training dataset (16 CT scans) using six statistical metrics (dice, jaccard, etc.). In Table 4, the segmentation results of the rectangles enclosing the abdomen are realized, and the results are obtained by comparing the rectangle template with the output of AbSeg.

Table 4.

Performance evaluation of AbSeg on the training dataset

Images Jaccard Dice Sensitivity Specificity CA Precision
Arterial_1 99.4741 99.7364 100 98.4158 99.5798 99.5945
Arterial_2 98.7221 99.3569 100 96.8136 99.0795 98.7221
Arterial_3 99.2613 99.6293 100 99.1291 99.5987 99.2613
Arterial_4 98.8633 99.4284 100 98.6689 99.3831 98.8633
Arterial_5 99.0488 99.5221 100 98.8644 99.4797 99.0488
Arterial_6 98.8743 99.4339 100 98.8006 99.4159 98.8743
Arterial_7 99.1987 99.5977 100 99.1811 99.5442 99.3731
Arterial_8 99.5635 99.7812 100 99.3070 99.6972 99.6914
Arterial_9 98.8133 99.4031 100 97.5640 99.1956 98.8133
Arterial_10 99.2672 99.6323 100 98.8434 99.5494 99.2672
Arterial_11 98.7896 99.3911 100 98.3925 99.3047 98.7896
Arterial_12 98.9234 99.4588 100 98.9337 99.4227 99.0667
Arterial_13 98.4711 99.2297 100 98.0364 99.1329 98.4711
Arterial_14 99.0381 99.5167 100 98.8736 99.4784 99.0381
Arterial_15 99.0081 99.5016 100 97.6150 99.2945 99.0081
Arterial_16 97.8232 98.8996 100 98.1300 98.9839 97.8232
Mean 98.9463 99.4699 100 98.4731 99.3838 98.9816

The best results are presented as italicized numbers

As seen in Table 4, AbSeg achieves reliable segmentation results with high scores for all performance metrics. This means that AbSeg performs the task in different circumstances by obtaining high success rates (especially for dice and jaccard) of approximately 99%.

In Table 5, the importance of operators is clarified by extracting the parts, and by running the system on different images. Tables 6 and 7 compare the performance comparisons of three thresholding techniques in terms of obtaining the abdomen section for low-quality and qualified edged abdomens. Higher rates are italicized in the tables.

Table 5.

Performance evaluation of the AbSeg sections

Performance without sections (with/without) Image Jaccard Dice CA
Image dilation Arterial_12 98.92/82.09 99.46/90.16 99.42/90.49
Clarification and denoising (median filter) Arterial_16 97.82/96.72 98.90/98.33 98.98/98.45
Obtainment of gradient magnitude Arterial_13 98.47/97.83 99.23/98.90 99.13/98.76
Morphological closing Arterial_12 98.92/81.77 99.46/89.97 99.42/90.32
Contrast-limited adaptive histogram equalization Arterial_12 98.92/84.95 99.46/91.86 99.42/91.99
Zerocross cleaning (ZE) Arterial_5 99.05/98.90 99.52/99.45 99.48/99.40

The best results are presented as italicized numbers

Table 6.

Performance comparison of the three techniques on low-quality edged abdomen

Thresholding approaches (Arterial_12) Jaccard Dice Sensitivity Specificity CA Precision
Binarization 98.40 99.19 99.85 98.33 99.14 98.54
Otsu 93.89 96.85 94.36 99.44 96.74 99.48
Spatial fuzzy C-means clustering 98.92 99.46 99.85 98.93 99.42 99.07

The best results are presented as italicized numbers

Table 7.

Performance comparison of the three techniques on qualified edged abdomen

Thresholding approaches (Arterial_4) Jaccard Dice Sensitivity Specificity CA Precision
Binarization 97.89 98.93 100 97.50 98.84 97.89
Otsu 98.86 99.43 100 98.67 99.38 98.86
Spatial fuzzy C-means clustering 98.86 99.43 100 98.67 99.38 98.86

The best results are presented as italicized numbers

As seen in Table 5, every section benefits from segmentation, and these operators remain essential for an accurate segmentation process. In addition, the percentage differences between with/without parts prove the efficiency of operators in AbSeg. At this point, the most important sections are presented in Table 5, meaning that other parts do not work for all images. This means that other parts are operated for the elimination of different problems. In Table 5, Arterial_12 stands for the image containing a low-quality (edged) abdomen.

In Tables 6 and 7, performance comparison is presented using the outputs of binarization, Otsu [24], and SFCM approaches.

As seen in Table 6, SFCM always outperforms Otsu and binarization algorithms in terms of achieving better dice, jaccard, and CA rates on images including low-quality edged abdomen. As seen in Table 7, the performance of SFCM and Otsu is the same for qualified edged abdomens, and their performance draws a graph that remains better than the binarization process. Consequently, it is inferred that the SFCM algorithm seems the best choice, since it achieves optimum results for both low-quality and qualified edged abdomen images.

After trials based on the training dataset, the proposed pipeline was implemented on test data that was completely diverse from the training set. Table 8 shows the performance evaluation of AbSeg for test data.

Table 8.

Performance evaluation of AbSeg on the test dataset

Images Jaccard Dice Sensitivity Specificity CA Precision
Arterial_17 99.3226 99.6602 100 99.2613 99.6454 99.3226
Arterial_18 99.5436 99.7713 100 99.1761 99.7054 99.5436
Arterial_19 99.4610 99.7297 100 99.0167 99.6506 99.4610
Arterial_20 97.6528 98.8125 100 97.1643 98.6991 97.6528
Arterial_21 98.5149 99.2519 98.8342 99.5450 99.1299 99.6731
Arterial_22 99.6077 99.8035 100 99.0005 99.7175 99.6077
Arterial_23 99.3692 99.6836 100 99.3760 99.6853 99.3692
Arterial_24 99.5604 99.7797 100 99.4773 99.7606 99.5604
Arterial_25 99.5505 99.7747 100 99.4889 99.7603 99.5505
Arterial_26 99.5392 99.7691 100 99.3729 99.7337 99.5392
Arterial_27 99.6599 99.8297 100 99.5762 99.8110 99.6599
Arterial_28 99.4008 99.6995 100 99.3165 99.6797 99.4008
Arterial_29 99.6809 99.8402 99.8988 99.6017 99.7935 99.7817
Arterial_30 99.6735 99.8365 100 99.5579 99.8119 99.6735
Arterial_31 99.5726 99.7858 99.8829 99.4332 99.7234 99.6890
Arterial_32 99.6976 99.8486 100 99.2685 99.7856 99.6976
Mean 99.3629 99.6798 99.9135 99.2271 99.6308 99.4489

The best results are presented as italicized numbers

As seen in Table 8, AbSeg presents a remarkable performance by achieving a 99.5% average rate for six different metrics and it obtains better results for the test dataset than the training set. The average rates for training/test were 98.95/99.36% (jaccard), 99.47/99.67% (dice), 100/99.91% (sensitivity), 98.47/99.23% (specificity), 99.38/99.63% (classification accuracy), and 98.98/99.45% (precision). In other words, AbSeg proved its efficiency by increasing the rates of jaccard, dice, specificity, CA, and precision on a different dataset than that utilized in the parameter settings.

Figure 6 shows the output of pipeline on different sections to demonstrate the visual results of different characterized images.

Fig. 6.

Fig. 6

Visual results obtained from different AbSeg sections (from left to right: original image, dilated image, closed image, output of SFCM, and output of AbSeg)

As seen in Fig. 6, the first image includes an abdomen scan in which the edges of the abdomen are very close to the edges of the scan. The second image involves the appearance of a bed section in the CT surrounding the bottom side of the abdomen and the third image includes an abdomen in which the edges of the abdomen are discontinuous.

As seen in the visual results, morphological processes fulfill their tasks without any disruptive effect on the abdomen, and this situation is confirmed by the output images. Besides, the effectiveness of AbSeg can be seen with regard to its operation as immune to different handicaps. In other words, the optimum and statistical structure can be obtained for changeable situations that can prevent the abdomen’s segmentation.

In the last part of the visual results, every image is resized to 550 × 700 since it is revealed as the best ratio in trials between the abdomen’s vertical and horizontal structure.

After training, tests, and visual results, a validation process is performed to confirm the performance of AbSeg on a new data taken from The Cancer Imaging Archive (TCIA) [14, 15]. For this, 26 CT images were randomly chosen from a dataset that included different types of scans (PET/CT, chest CT, abdominal CT, and pelvis CT images). The data used consists of 12 abdominal CTs, 11 chest CTs, and three pelvis CT images. Herein, the performance of AbSeg is evaluated on chest CT and pelvis CT images besides abdominal-based trials, for whether it can be utilized on the segmentation of similar (ellipsoid) shaped ROIs (chest CT and pelvis CT images).

In the training and test images, the used data is 852 × 852, whereas it is chosen as 1920 × 1080 in the validation dataset. At this point, the image resolution is examined to reveal whether it positively affects the segmentation results. Figure 7 shows the utilized image types, Table 9 shows the results of the AbSeg pipeline on validation data, Table 10 presents the average performance obtained for different types of CT image, and Table 11 reveals the average performance on the data for different resolutions (training and test validation).

Fig. 7.

Fig. 7

The different types of CT image used in the validation set

Table 9.

Performance evaluation of AbSeg in the validation dataset

Images Jaccard Dice Sensitivity Specificity CA Precision Kind of CT Scan
Image_1 99.6617 99.8306 100 99.8193 99.8821 99.6617 Abdomen
Image_2 99.4603 99.7294 100 99.7984 99.8530 99.4603 Abdomen
Image_3 99.7768 99.8883 100 99.9639 99.9689 99.7768 Abdomen
Image_4 99.6630 99.8312 99.8812 99.8508 99.8631 99.7813 Pelvis
Image_5 99.4940 99.7464 99.8930 99.8226 99.8442 99.6001 Chest
Image_6 99.6902 99.8448 99.6902 100 99.9374 100 Abdomen
Image_7 99.5080 99.7534 99.6493 99.9317 99.8400 99.8578 Chest
Image_8 99.5626 99.7808 100 99.8857 99.9093 99.5626 Chest
Image_9 99.2873 99.6424 99.7758 99.9058 99.8849 99.5094 Pelvis
Image_10 99.7761 99.8879 100 99.8537 99.9115 99.7761 Chest
Image_11 99.6485 99.8239 99.8713 99.8541 99.8609 99.7766 Chest
Image_12 99.6514 99.8254 100 99.8301 99.8857 99.6514 Abdomen
Image_13 99.6115 99.8054 100 99.8017 99.8685 99.6115 Abdomen
Image_14 99.5779 99.7885 99.8856 99.7638 99.8166 99.6916 Chest
Image_15 99.6063 99.8028 99.8924 99.9045 99.9015 99.7133 Chest
Image_16 99.4921 99.7454 99.6810 99.9642 99.9192 99.8099 Abdomen
Image_17 99.3393 99.6685 99.8051 99.8967 99.8801 99.5324 Abdomen
Image_18 99.5663 99.7827 100 99.6938 99.8202 99.5663 Chest
Image_19 99.5410 99.7700 99.8667 99.8052 99.8281 99.6735 Chest
Image_20 99.5526 99.7758 99.8819 99.7938 99.8276 99.6699 Chest
Image_21 99.6389 99.8191 100 99.8450 99.8914 99.6389 Abdomen
Image_22 99.4790 99.7388 100 99.7879 99.8491 99.4790 Pelvis
Image_23 99.5120 99.7554 99.8534 99.8273 99.8360 99.6576 Chest
Image_24 99.6653 99.8324 100 99.8144 99.8804 99.6653 Abdomen
Image_25 99.4734 99.7360 100 99.7937 99.8516 99.4734 Abdomen
Image_26 99.6770 99.8382 100 99.8031 99.8775 99.6770 Abdomen
Mean 99.5736 99.7863 99.9087 99.8466 99.8726 99.6644

Table 10.

Average performance of AbSeg for different types of CT images

Kind of CT scan Jaccard Dice Sensitivity Specificity CA Precision
Abdomen (12 images) 99.5948 99.7970 99.9314 99.8609 99.8913 99.6632
Chest (11 images) 99.5768 99.7880 99.8903 99.8306 99.8542 99.6859
Pelvis (3 images) 99.4764 99.7375 99.8857 99.8482 99.8657 99.5899

Table 11.

Performance evaluation of AbSeg for different resolutions

Dataset Jaccard Dice Sensitivity Specificity CA Precision
Training (16 images) 98.9463 99.4699 100 98.4731 99.3838 98.9816
Test (16 images) 99.3629 99.6798 99.9135 99.2271 99.6308 99.4489
Validation (26 images) 99.5736 99.7863 99.9087 99.8466 99.8726 99.6644

In Fig.7, the abdomen, chest, and pelvis areas change as they are diverse from one another, and the shape of the ROIs seems non-proportional. As it differs from the training and test datasets, the validation set includes high-resolution CT images. Herein, PET/CT images are not included in the validation data because the segmentation operation of these images constitutes another qualified paper because PET/CT images include the arm parts of patients, which prevent the accurate segmentation of ellipsoid ROIs.

According to Table 10, the segmentation performance of AbSeg remains remarkable for the abdomen and it can segment the abdomen better than the chest and pelvis CT images for all metrics. In future evaluation, its usage may be extended to chest and pelvis CT images in terms of including an ellipsoid ROI as in abdomen CT images, but the amount of data should be increased to generalize the performance of AbSeg to chest and pelvis CT images. Currently, it can be seen that AbSeg can segment abdominal CT images with a high success rate, and its performance seems remarkable for similar shaped ROIs and the chest and pelvis areas.

Table 11 reveals that AbSeg performs the task of abdomen segmentation on different datasets with higher performance for approximately all metrics (except sensitivity) compared to trials realized on training and test datasets. It can be concluded here that the resolution quality directly affects the system’s output, and a higher resolution is required to attain better segmentation results.

Conclusions

This study contains an efficient and statistical structure for the optimum segmentation of the abdominal region in CT images. According to the results, AbSeg performs the task of abdomen segmentation under different handicaps that occur in abdominal CT images. Besides, it obtained very high segmentation results for the above six statistical metrics, with respective rates of 98.95, 99.2, and 99.57% for the training, test, and validation sets. Moreover, it was not affected by the appearance of the bed section, patient information, narrow histogram, poor contrast, or low-quality edges by processing sequential operators that eliminate the handicaps. In addition, AbSeg is designed using optimal operators and parameters for the segmentation of the abdominal region.

Table 12 shows the literature comparison of studies based on abdomen segmentation. Herein, it should be remembered that the results of AbSeg can be obtained on the segmentation of the rectangle surrounding the abdomen part. In other words, the results are based on a rectangle ROI that surrounds the abdomen, meaning that a higher performance is achieved in the event that the rectangle overlaps the edges of the abdomen.

Table 12.

Performance evaluation of AbSeg sections

Method Task Method Dice (%) CA (%)
Koss et al. [1] Skin and colon segmentation Hopfield Neural Network (HNN)-based hybrid approach 91
Xu et al. [7] Abdomen wall segmentation Multi-atlas label fusion 89
This study Abdomen segmentation AbSeg 99.64 99.61

In future work, we want to use AbSeg to detect and segment different types of tumors that can occur in the abdomen. Moreover, the usage of AbSeg will be extended to segment different regions in other scan types.

Acknowledgements

This work is supported by the Coordinatorship of Selcuk University’s Scientific Research Projects.

Contributor Information

Hasan Koyuncu, Phone: + 90 332 223 37 15, Email: hasankoyuncu@selcuk.edu.tr.

Rahime Ceylan, Email: rpektatli@selcuk.edu.tr.

Mesut Sivri, Email: drmesutsivri@gmail.com.

Hasan Erdogan, Email: dr.hasanerdogan@gmail.com.

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