The process of segmentation of the cardiac image aims to limit the inner and outer walls of the heart to segment all or portions of the organ’s boundaries. Due to its accurate morphological information, magnetic resonance (MR) images are typically used in cardiac segmentation as they provide the best contrast of soft tissues. The data acquired from the resulting cardiac images simplifies not only the laboratory assessment but also other conventional diagnostic techniques that provide several useful measures to evaluate and diagnose cardiovascular disease (CVD). Therefore, scientists have offered numerous segmentation schemes to remedy these issues for producing more accurate diagnosis. This work conducts a comparative study among several medical image segmentation schemes to find the most accurate segmentation quality based on performance measurements such as Hausdorff distance, peak signal-to-noise ratio (PSNR), and similarity Dice coefficient. This paper utilizes a multi-axis Cardiac Magnetic Resonance Image (CMRI) database in three axes for several case studies which provide the results of various segmentation schemes. Additionally, throughout the experiments, the performance time of every segmentation scheme is estimated and utilized in the comparison process as an additional performance factor.
The detection of CVD is vital as there may be a shortage of experienced surgeons when needed. Fortunately, any issues that arise in the cardiovascular task or the cardiac cycle are reproduced in the changing shape of the left ventricle (LV). This particular symptom can be used in monitoring cardiovascular function and can detect potential heart disease. Therefore, an analysis of the human cardiac function requires a detailed explanation of the shape and structure of the left ventricle.
Image segmentation is considered a significant stage in medical image processing. The image segmentation process includes splitting the MR image into different sets of pixels with identical features. The bottom-up methods consist of a set of conventional image processing methods, such as morphological operations and contour detection, which are systematically utilized for image segmentation [
The Level Set Method (LSM) may be considered as a well-established method for performing image segmentation. It relies on the structure that utilizes the concept of level set lefts like the numerical analysis described with shape and surface tracking. LSM is able to track topology alternations, which is the primary drawback of ACMs. Therefore, LSM may be considered the optimal segmentation approach. However, level calculations require complicated methods, such as LSM having runtime control issues and requiring an optimal initialization, compounded with the high calculation costs and probability of being trapped in the local minimum [
The paper remainder is structured as follows: Section 2 presents a general classification of medical image segmentation techniques. Section 3 presents the investigated medical image segmentation schemes. In section 4, key performance indicators utilized to compute the segmentation quality are presented. Section 5 represents the experimental results. Comparative results are explored and discussed in section 6 and discussions. In the end, section 7 concludes the paper.
Image segmentation has various applications including in the biomedical field. Accurate segmentation techniques are beneficial in aiding physicians and specialists in the early diagnosis of numerous diseases and therefore can begin to monitor patient conditions appropriately. MR modality is a radiology tool for visualizing human organ structures in detail [
Various techniques have been introduced to solve the medical segmentation problem. Medical image segmentation techniques are classified as: manual segmentation, thresholding techniques, edge-based techniques, region-based techniques, and graph-based techniques.
Manual segmentation requests a user to manually trace the desired boundaries. Usually special hardware and software supporting user interactions are required. The advantage of manual method is that the user can make full use of their medical knowledge. In many cases, experts’ results are treated as the ground truth however, manual segmentation is an exceptionally time consuming, tedious, and laborious endeavor. In addition to the extraneous work involved, manual results tend to be inconsistent across different users. To solve this issue, averaging several users’ results is encouraged and thus it will increase the processing time greatly. So, a post-processing procedure to streamline the user defined boundaries is optional.
Thresholding techniques make decisions by partitioning the image histogram into several parts. Pixels that fall in a certain intensity range are classified into the same group. The main drawback of this technique is that it cannot perform cardiac LV segmentation accurately as only intensity histogram is used, but spatial information is ignored. Thus, morphological operations are usually needed to post-process the segmentation results of threshold techniques [
Edge-based techniques depend on the discontinuity in the features of the image between different regions corresponding to high intensity gradient values. It is a good indicator of the boundary locations when edges are prominent in the image. For instance, Caselles geodesic ACM [
Region-based techniques [
Graph-based techniques are also called pixel-based techniques. Since edge and area-based methods have limitations, it is desirable to use techniques that take the pixel information in the whole image into consideration. A graph-based technique considers the image as a graph of vertices and edges. This allows it to use both boundary and region information in image through the relationships between neighbors for every pixel to obtain better results especially in medical segmentation [
This section presents the implemented techniques through this research.
Caselles technique is an active contour edge-based technique. The force function is estimated using the image gradient. The curve is obtained in the high gradient regions. The regularization is intrinsic in this technique. There is not a regularization term [
where
A Chan-Vese technique is an ACM without edges or LSM [
In LSM, the curve is characterized by the set of zero levels of the level-set function that controls the curvature movement. LSM has elastic behavior due to the smoothness and flexibility of the level set function [
where
A data attached term is incorporated in the first integral of the above energy function, while the regularization term is represented by the second integral to smooth the contour. The velocity term represents the image features of the object.
where
The curve evolution is estimated using PDE will be as follows:
where
The Lankton segmentation technique is a region-based ACM [
where E is the energy function in Lankton technique,
In the Lankton-Chan Vese, the feature
while in the Lankton-Yezzi, the feature
where,
The evolution equation,
where the feature using the Lankton-Chan Vese is as follows:
And in the Lankton-Yezzi, the feature will be as follows:
where
when
The Shi-Karl segmentation technique is a fast technique that depends on a level-set based curve evolution approximation [
The two narrow-bands of neighboring points near the curve are stored into lists
where
The Gaussian filter is of the form:
The evolution of the curve is done along with a smoothing speed
where
where
The Li segmentation technique is a region-based ACM [
where
For a scale parameter
The following equation represents the curve evolution,
Because of
This technique is a variational B-spline region-based LSM [
The evolving curve
where
The implicit function
The evolution equation corresponds to every coefficient
where
The level set evolution is obtained as:
where
To assess the performance of medical image segmentation methods, several segmentation performance metrics are utilized such as Dice Metric (DM), PSNR and Haussdorff distance (HS) [
The DM can be expressed as [
DM is a number between 0 and 1, where 0 and 1 indicates no overlapping at all and full overlapping.
The PSNR can be mathematically expressed as [
The Haussdorff distance is utilized for measuring the distance among subgroups of a metric space. The HS can be expressed as [
where
The experimental results are categorized into two distinct sections to underline both the segmentation and psoriasis lesion localization results. The presented results of this paper were obtained through using different 6 techniques to 300 sets of CVD. In this paper, multi-axis CMR database was used in three axes for 6 case studies to provide the results of various segmentation schemes. In this section, the employed techniques for studying and segmenting medical images are executed using MATLAB.
This section compares the performance of the studied medical image segmentation techniques. The segmentation quality is measured through comparing the resulted slices of different schemes with manual segmented slices using DM, PSNR, HS, and the execution time. The key performance matrices mean values of LV segmentation techniques are given in
The summery of the obtained results is explained in
Shi-Karl and Chan-Vese techniques give good results in term of both the DM and computation time, but they don’t take the characteristics of the edges in consideration. Although the Shi-Karl technique gives better results in the equality values, the final outline of the Chan-Wes technique seems smooth due to the decryption of the level playing function used in the Shi-Karl technique. The DM column in
CMR Slice NO. | Segmentation technique | Mean DM | PSNR | Mean HS | Mean Execution time |
---|---|---|---|---|---|
CMR Slice 1 | Caselles | 0.924 | 20.16 | 15.662 | 3.063245 |
Chan-Vese | 0.964 | 22.38 | 9.962 | 2.506578 | |
Li | 0.434 | 9.52 | 150.872 | 8.163245 | |
Lankton | 0.964 | 22.30 | 11.902 | 3.313245 | |
Bernard | 0.264 | 5.93 | 149.822 | 131.4166 | |
Shi-Karl | 0.964 | 22.55 | 7.612 | 2.099911 | |
CMR Slice 2 | Caselles | 0.914 | 20.70 | 12.302 | 3.223245 |
Chan-Vese | 0.924 | 21.08 | 8.962 | 3.043245 | |
Li | 0.394 | 9.34 | 151.712 | 8.086578 | |
Lankton | 0.934 | 21.21 | 9.902 | 3.489911 | |
Bernard | 0.254 | 6.26 | 159.422 | 97.78325 | |
Shi-Karl | 0.924 | 20.98 | 8.962 | 1.903245 | |
CMR Slice 3 | Caselles | 0.884 | 22.33 | 8.712 | 3.269911 |
Chan-Vese | 0.954 | 22.49 | 7.302 | 2.979911 | |
Li | 0.364 | 8.86 | 154.542 | 8.029911 | |
Lankton | 0.944 | 24.02 | 5.902 | 4.019911 | |
Bernard | 0.244 | 6.36 | 162.242 | 89.30658 | |
Shi-Karl | 0.954 | 22.25 | 7.972 | 2.073245 | |
CMR Slice 4 | Caselles | 0.874 | 18.96 | 18.702 | 3.213245 |
Chan-Vese | 0.994 | 25.69 | 5.022 | 2.976578 | |
Li | 0.374 | 9.07 | 153.062 | 7.939911 | |
Lankton | 0.934 | 21.71 | 9.902 | 3.429911 | |
Bernard | 0.264 | 6.42 | 160.692 | 93.71991 | |
Shi-Karl | 0.994 | 25.26 | 5.142 | 2.063245 | |
CMR Slice 5 | Caselles | 0.754 | 16.62 | 17.392 | 3.909911 |
Chan-Vese | 0.984 | 24.90 | 5.372 | 3.256578 | |
Li | 0.384 | 9.95 | 157.172 | 9.326578 | |
Lankton | 0.894 | 20.35 | 12.902 | 3.913245 | |
Bernard | 0.224 | 6.09 | 164.432 | 72.36325 | |
Shi-Karl | 0.974 | 24.62 | 5.902 | 2.026578 | |
CMR Slice 6 | Caselles | 0.954 | 23.31 | 10.752 | 2.893245 |
Chan-Vese | 0.994 | 26.10 | 5.022 | 2.499911 | |
Li | 0.394 | 10.23 | 158.112 | 8.633245 | |
Lankton | 0.964 | 24.05 | 8.712 | 3.129911 | |
Bernard | 0.224 | 6.19 | 165.822 | 109.9433 | |
Shi-Karl | 0.994 | 26.43 | 5.022 | 1.583245 |
Segmentationtechnique | Systole phase | Diastole phase | ||||||
---|---|---|---|---|---|---|---|---|
DM | PSNR | HS | Execution time | DM | PSNR | HS | Execution time | |
Caselles | 0.8906 | 20.4466 | 15.239 | 3.858478 | 0.8423 | 19.5841 | 14.956 | 3.858478 |
Chan-Vese | 0.9673 | 22.5383 | 8.544 | 3.708178 | 0.9615 | 22.6916 | 8.673 | 3.708178 |
Li | 0.3948 | 21.2941 | 159.882 | 13.45968 | 0.3048 | 7.9966 | 164.699 | 13.45968 |
Lankton | 0.8781 | 20.82 | 15.269 | 4.942078 | 0.8648 | 19.66 | 14.941 | 4.942078 |
Bernard | 0.2848 | 19.175 | 161.582 | 208.9577 | 0.2098 | 5.8391 | 166.881 | 208.9577 |
Shi-Karl | 0.9765 | 22.5758 | 7.367 | 2.632378 | 0.9641 | 23.1866 | 8.826 | 2.632378 |
In this research paper, the performance of the studied segmentation techniques is evaluated and compared using a multi-axis 3D multi-layer CMRI dataset using several key performance indicators such as PSNR, DM, and HS. During the experiments, the same 3D short-axis multilayer CMR dataset is used for various case studies to illustrate the results of such segmentation techniques. In the range of the tested datasets and examined segmentation techniques, it is noticed that the Shi-Karl segmentation technique is the best due to the resulted segmentation quality and segmentation time points of view. The Chan-Vese technique occupies the second rank from the segmentation quality and segmentation time points of view. Its results are particularly close to the Shi-Karl technique results. However, LV blood pool segmentation quality obtained by the Li and Bernard segmentation techniques is the worst. Such techniques also consume the longest segmentation time. Therefore, in the range of the tested datasets and the examined segmentation techniques, we can state that the Li and Bernard segmentation techniques cannot separate the LV cavity from other parts of the image. According to these results, the Shi-Karl and Chan-Vese techniques are the optimal choice among the tested segmentation techniques in separating the LV cavity from other parts of the CMR images.
This study was funded by the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/08), Taif University, Taif, Saudi Arabia.