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Automated biomedical image analysis devices are facing some challenges due to the increasing quality of biomedical images.
A high amount of spatial information creates severe problems for automated and computer-aided diagnostic systems because medical diagnostic systems demand quick and accurate results.
Image segmentation plays a vital role in many automated computer-aided image analysis systems. It is essential to generate precise reports within the stipulated amount of time to provide accurate treatment to the patients. To handle this situation effectively and to accelerate the screening process of the COVID infection, a superpixel-based novel approach is proposed in this work to segment the CT scan images.
Superpixels are useful to represent a set of pixels in a computation-friendly manner. Different approaches can be found in the literature to find the superpixel image from an input image [54] , [55] , [56]. Some superpixel computation methods like mean shift [54] and watershed [56] produce irregular superpixels and some methods like SLIC [55] generate regular superpixels. Meanshift and watershed approaches are more useful due to the capability to generate irregular superpixels.
The watershed approach is simpler to implement compared to the mean-shift approach but it is sensitive to the noise which is not at all desirable for the image segmentation approaches.
In this work, the noise sensitivity of the watershed approach is removed with the help of the gradient image, which is generated using the approach, proposed in [57].
The obtained gradient image is processed using the morphological erosion and dilation-based reconstruction operations, which are given in Eqs.
Here, s e is the structuring element and it is an important parameter that controls the segmented outcome. The size of the structuring element is subjective and depends on the image under consideration. Practically, it is not possible to determine different structuring elements which are of various sizes, depending on the image.
Therefore, the pointwise maximum value is computed using Eq. The number of superpixels is inversely dependent on the size of the structuring elements.
It can be easily understood in Fig. The image considered in these two figures is the T e s t 01 image [58] please refer to Table 3. Dependency of the number of superpixels on the size of the disk structuring element a — h superpixel images obtained using the disk structuring element of size 3 to 10 respectively, i Size of the structuring element vs.
Dependency of the number of superpixels on the size of the square structuring element a — h superpixel images obtained using the square structuring element of size 3 to 10 respectively, i Size of the structuring element vs.
A very small lower bound is not desirable because it will produce very small regions and some essential edge information can be lost. So, the threshold value can be adjusted as per the requirement and depending on the available resources.
The conventional fuzzy C-means clustering approach often overlooks some important spatial information that can be costly in terms of the segmentation performance. Some approaches try to solve this problem by considering and blending some local spatial information in the objective function but it increases the computational cost and therefore not suitable on many occasions. Superpixels can help in this context by over-segmenting an image in many small, perceptually uniform, and homogeneous regions.
In this work, the CT images are first processed to determine the superpixels using the proposed approach and then the fuzzy artificial cell swarm optimization approach is used to determine the segmented image by finding the optimal clusters. As discussed earlier, the type 2 fuzzy system is used to perform the segmentation. The fuzzy objective function which is given in Eq.
To incorporate the advantages of the superpixel, it is necessary to modify the fuzzy objective function.
The representative value is used in the objective function, and the modified objective function is given in Eq. The cluster centers can be updated and guided by the artificial cell swarm optimization and therefore, no equation is required to compute the updated positions of the cluster center.
This approach is not dependent on the selection of the initial cluster centers. The proposed procedure is given in algorithm 3 and the schematic flow diagram is given in Fig. As discussed earlier, the properly annotated ground truth segmented images may not be available always, and therefore, some standard intrinsic cluster evaluation methods are used here to evaluate the proposed approach quantitatively.
The proposed methods are applied to the images and the test results are demonstrated with the 10 CT scan images that are randomly selected which are obtained from different countries of the world. Table 3 gives a brief overview of the test images and the test images along with their histograms are given in Fig.
The experiments are performed in the MatLab Ra on a computer that is equipped with an Intel i3 processor and 4 GB main memory. The proposed method is compared with some metaheuristic optimization-based image segmentation approaches like modified genetic algorithm [67] , modified PSO [68] , improved bat algorithm [69] and modified cuckoo search method [70] in both qualitative and quantitative manner. The visual comparison is presented in Fig.
The acceptable values are highlighted in boldface. The comparisons and evaluations are performed for different numbers of clusters. A comparative study of different approaches using T e s t 01 for different number of clusters. Performance evaluation of different approaches using Davies—Bouldin index The highlighted values indicates acceptable values. Performance evaluation of different approaches using Xie—Beni index The highlighted values indicates acceptable values.
Performance evaluation of different approaches using Dunn index The highlighted values indicates acceptable values. From the qualitative and quantitative results, it can be observed that the proposed SUFACSO approach outperforms some state-of-the-art works and can produce realistic outputs that are certainly helpful for the interpretation of the real-life CT scan images and therefore, this approach can be helpful for the early screening purposes.
At the end of each table, the average performance of the five approaches is reported which is beneficial to understand the overall performance of these methods for the different number of clusters and different cluster validity indices. In the case of average, the column-wise optimal values are highlighted instead of highlighting the row-wise optimal values.
The row-wise highlighted values talk about the performance of the individual algorithm for the different number of clusters whereas the column-wise highlighted values help to understand the performance of the individual algorithms. It can be observed that the proposed approach outperforms other approaches for most of the number of clusters as well as for most of the validity indices. For example, on a total of 16 occasions i.
These comparative results are graphically presented in Fig. In X -axis the number of clusters and in the Y -axis, the values of the corresponding validity index are plotted.
The experiments are carried out for the different numbers of clusters. A particular approach may perform well for a particular cluster count. That is why the average values of all experiments are reported at the end of each table for better interpretation.
It can be observed that the proposed approach can optimize different objective functions effectively. Actually, the experiments are carried out on CT images in the first phase and CT images in the second phase. It is already mentioned in Section 5. Results that are obtained from all images are not possible to report in this stipulated amount of space. Therefore, only some results that are obtained from some selected images are reported. Apart from these tests, the proposed approach is also compared with some of the active contour models based on some standard parameters like accuracy, precision, and recall.
This comparison is performed by using the database that is available at [71]. This dataset is created by collecting sample images from 49 patients with age range 32—86 years. The obtained average results are reported in Table 8. The rate of convergence is an important parameter to be studied. The performance evaluation remains incomplete without studying and comparing the convergence of different algorithms.
The convergence analysis gives a clear view of the comparative performance of different algorithms for the different numbers of clusters. The graphical analysis of the convergence is presented in this subsection using the image T e s t 01 for the Dunn index.
In Fig. In a single plot, four separate curves are indicating four different clusters. These curves show that the proposed approach can efficiently segment the images for a higher number of clusters. Moreover, the proposed approach also outperforms some other methods in terms of convergence besides quantitative and qualitative performance. The time complexity is an important aspect that is to be analyzed. From the detailed discussion of the proposed approach, it can be noticed that the proposed approach can be viewed as a two-phase procedure where the watershed-based computation approach is used to determine the superpixel image from the underlying image in the first phase and the optimal segmented outcome is computed in the second phase.
The task of optimization is performed using the proposed fuzzy ACSO approach. The gradient information of an image is used to avoid the noise sensitivity of the water-shed based superpixel computation process. The watershed-based technique is a simple method to compute the superpixel and the implementation follows linear complexity [56].
It is quite inspiring and lucrative to adopt this approach on different occasions. In the optimization part, the fuzzy objective function is optimized by using the proposed fuzzy ACSO method. The ACSO approach is an effective and efficient approach that can be executed in linear time [47]. So, the proposed approach is efficient enough and can be effectively used in various real-life problem-solving scenarios. This approach can effectively process high-quality images with the help of the proposed superpixel-based approach that is an essential quality for the real-life application of an image segmentation approach.
This approach removes the dependency of choice of the initial cluster centers as well as the ACSO approach determines the optimal cluster centers by optimizing some validity indices. These advantages motivate us to apply the proposed approach to automatically segment the radiological images that will be certainly helpful in diagnosing some symptoms of COVID The experimental outcomes show the efficiency of the proposed approach.
Under this pandemic environment, this work is designed hoping that it can help physicians and other domain experts to some extent in the early diagnosis of the disease.
Early diagnosis can prevent the drastic spread of this highly infectious virus. Quantitative results do not have any direct implications in real-life diagnosis. The segmented outcomes are useful in the diagnosis process. Physicians can investigate the segmented outcomes to find some prominent and common features as mentioned in Table 2. The segmented images will be helpful in the easy interpretation of the radiological images.
The proposed SUFACSO approach is an efficient image segmentation approach that can effectively segment the radiological images that highly useful in the easy interpretation of these images.
There is a high possibility that a suspected patient can spread the disease in the community completely unwillingly. The proposed approach can reduce this chance because an initial screening can be performed by the physicians comfortable with the help of the proposed SUFACSO approach. It is worth mentioning here that the proposed approach is neither a replacement of the RT-PCR test nor it can confirm the presence of the virus accurately.
However, this approach can be helpful in an initial screening at an early stage that will restrict the spread of this highly infectious virus by separating suspected patients from the rest of the community.
The obtained results indicate that the proposed approach is suitable for real-life scenarios and also performs efficiently. This approach can be easily adapted for the automated screening purposes of the COVID infected patients. It is assumed the quality of the CT scan images is considerably high and the performance of the proposed approach is not verified against the presence of noise. It will be interesting to study the proposed approach in the presence of noise.
The scalability of the proposed approach to different types of biomedical images can be explored in future studies. Missing manual annotations can jeopardize the generalizability of the proposed work. On the other hand, the obtained results are quite promising and encouraging. From the best of the knowledge of the authors, there is no publicly available manually annotated dataset for the chest CT scan images of the COVID positive cases.
Although the proposed approach is efficient enough to segment the CT scan images automatically and produces realistic segmented outcomes still, some important drawbacks can be observed in this proposed approach that can be addressed in the subsequent works.
One important drawback of the proposed approach is that it cannot automatically determine the number of clusters and it can be overcome in future works. Automated estimation of the clusters can make this approach more realistic, robust, and application friendly. The proposed method can handle only a single objective at a time.
Therefore, the proposed approach is not suitable for multi-objective optimization issues unless enhanced further. The number of images in the dataset is not very large. So, the proposed approach can also be tested on some additional CT images of COVID infection as well as on some standard dataset of the biomedical images.
It neither use any training dataset nor uses any pre-trained model. The proposed approach can effectively segment the radiological images that are collected from different patients i. It is to be clarified that this approach cannot take any decision about the type of disease automatically. For example, the proposed approach cannot automatically differentiate between COVID related lung images and other lung diseases. This approach aims to help physicians in early and quick interpretation of the radiological images and diagnosis of the diseases without any manual delineations.
This article proposes a novel, simple and elegant solution that uses some of the important features of the chest CT scan images to screen the COVID suspected patients easily and at an early phase which can be considered as an effective tool to reduce the drastic spread of this virus. From Fig. Both qualitative and quantitative study produces some satisfactory results which help to make the proposed approach trustworthy so that it can be reliably adapted in the real-world scenarios.
The proposed approach initially performs a superpixel-based clustering using the proposed superpixel computation method which significantly reduces the computational overhead for the further clustering process by reducing a large amount of spatial information. Therefore, radiological images can be conveniently explicated with the application of the proposed method and the proposed approach is also helpful in the easy interpretation of the radiological images.
The proposed work neither claims that the suggested approach is cent percent accurate in determining the COVID infection nor claims that it can be a replacement of the RT-PCR test but, the proposed method can help detect some common characteristics from the CT scan images, that can help to isolate some suspected patients from the rest of the community. The proposed approach is helpful for the early screening of the COVID besides being a significant contribution to the image segmentation literature.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors would like to express their gratitude and thank the editors, anonymous reviewers, and referees for their valuable comments and suggestions which are helpful in further improvement of this research work.
The dedicated graphics memory is not utilized for any kind of processing purposes. The system in which the experiments are carried out is equipped with the Microsoft Windows 7 64 bit operating system. It is not at all essential to use the Matlab environment to implement the proposed approach. We have chosen Matlab due to the availability of some inbuilt functions which are helpful to reduce the coding complexity.
Still, any other languages or platforms can be used to implement the same. It is assumed that there are no manual annotations available and the proposed approach is capable to process the images without having any prior knowledge. The final segmented images are constructed by assigning the superpixel to their corresponding cluster centers.
These segmented images are helpful to interpret different features from these radiological images. Appl Soft Comput. Published online Feb 3. Author information Article notes Copyright and License information Disclaimer. All rights reserved. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.
Abstract Due to the absence of any specialized drugs, the novel coronavirus disease or COVID is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID patients.
Introduction Automated computer-aided systems prove their effectiveness and real-life applicability in various scenarios. Table 1 Some of the related literatures and their brief overview. The calibrated source-to-background curves are used to determine the volume using the iterative thresholding procedure.
One major drawback of the system is that it cannot effectively measure small volumes. Wiemker et. This work proposes a divergence theorem and histogram-based Ct image segmentation approach. This approach is can effectively and optimally isolate the lung nodules from the CT scan images.
In this context, the optimality is defined in terms of the mean gradient of the iso-surface and the sphericity. Asari et. This algorithm is consisting of two stages where the first stage employs a global thresholding approach and in the second phase, the differential region growing is used to extract the gastrointestinal lumen from the endoscopic images.
The dynamic hill-clustering approach is used to ascertain the effectiveness of the termination criteria and to look after the growth process. Yu-qian et. Traditional gradient-based edge detection approaches are susceptible to noise and therefore, this approach proposes a novel approach to detect edges of the lung CT scan images using mathematical morphology. This approach is tested on the CT images which are corrupted with the salt-and-pepper noise and its efficiency is proved by comparing this approach with some of the other standard approaches.
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