An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

Sasmal, Pradipta; Bhuyan, M. K.; Dutta, Soumayan; Iwahori, Yuji

doi:10.1016/j.patrec.2021.12.014

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…Most of the previous work in polyp classification uses supervised learning processes like a Hessian filter and Support vector machine (SVM), Recurrent Convolutional Neural Network (RCNN), etc. These methods have various demerits such as finding the optimal value of the threshold in the case of SVM, and poor segmentation due to irregularly shaped polyps for SA-DOVA [4]. In [4], the authors adopt an adaptive Markov random field (MRF) method which is an unsupervised learning method.…”

Section: B Related Workmentioning

confidence: 99%

“…These methods have various demerits such as finding the optimal value of the threshold in the case of SVM, and poor segmentation due to irregularly shaped polyps for SA-DOVA [4]. In [4], the authors adopt an adaptive Markov random field (MRF) method which is an unsupervised learning method. The method over-segments the image into super pixels which are then refined by Local Binary Pattern (LBP) and color features used in the adaptive MRF.…”

Section: B Related Workmentioning

confidence: 99%

“…Colonoscopy has traditionally been the gold standard for identifying colonic polyps [4]. Nevertheless, it can be difficult to find these polyps during a colonoscopy, and the examining physician may occasionally overlook them.…”

Section: Introductionmentioning

confidence: 99%

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ContourNet—An Automated Segmentation Framework for Detection of Colonic Polyps

Pathan,

Somayaji,

Ali

et al. 2024

IEEE Access

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One of the most prevalent forms of malignant tumor affecting the digestive system is the Colorectal Cancer (CRC). Due to the recurrent nature of occurrence of CRC tumors the morbidity and mortality rate associated with CRC tumors is very high, making it the fourth leading cause of cancer worldwide. Although colonoscopy, is considered as pre-dominant screening mechanism for detection of CRC, over the recent years several researchers have attempted in developing AI diagnostic tools for segmenting colonic polyps. However, manual intervention and decreased accuracy rate is a major drawback witnessed by these approaches. In this study, we propose a novel automated colonic polyp segmentation mechanism using U-Net and chroma based deformable model termed as ContourNet . The proposed model eliminates the need for manual identification of region of interest irrespective of the high degree of variability between the lesion and non-lesion pixels. The model considers the chromaticity and statistical information from the identified region of interest to evolve the contour close to the affected area. The proposed algorithm was developed using Clinic CVC dataset for training, and a test accuracy of 94% was obtained. The generalization ability of the proposed design is validated on three different datasets, considering all the images as test set, a high degree of accuracy was obtained in identifying the affected regions. Thereby the results obtained prove the ability of the proposed method to be adopted in developing a CAD tool in detection of pathologies associated with gastroenterology in contrast to the state of art methods reported in literature.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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ContourNet—An Automated Segmentation Framework for Detection of Colonic Polyps

Pathan,

Somayaji,

Ali

et al. 2024

IEEE Access

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“…Many image segmentation algorithms have been proposed in the literature, from the traditional techniques, such as thresholding [ 30 , 31 , 32 , 33 ], edge-based segmentation [ 34 , 35 ], histogram-based bundling, region-based segmentation [ 36 , 37 , 38 , 39 ], clustering-based segmentation [ 40 , 41 , 42 , 43 , 44 ], watershed methods [ 45 , 46 , 47 , 48 , 49 ], to more advanced algorithms such as active contours [ 50 , 51 , 52 , 53 ], graph cuts [ 54 , 55 , 56 , 57 ], conditional and Markov random fields [ 58 , 59 , 60 , 61 ], and sparsity-based methods [ 62 , 63 , 64 ].…”

Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

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“…The performance of the deep learning model for polyp segmentation was compared by Trinh et al [31], the best accuracy network required the high enough performance devices. An adaptive Markov random field was also proposed by Sasmal et al [32] for the polyp segmentation task, this is an unsupervised learning method. The advantage of this approach is the smaller consumption time, however, the network efficiency is not better than the supervised method.…”

Section: Introductionmentioning

confidence: 99%

Automatic Polyp Segmentation Using Modified Recurrent Residual Unet Network

et al. 2022

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Colorectal cancer is a dangerous disease with a high mortality rate. To increase the likelihood of successful treatment, early detection of polyps is a useful solution. The Unet-architecture network model is showing success in medical image segmentation including analysis of polyps from colonoscopy images. Traditional Unet and Unet-based models are often huge, requiring training and deployment with a highperformance system. Designing models with compact size and high performance would be an important goal. In this study, we proposed to modify the Residual Recurrent Unet architecture to improve the size of the model while ensuring the model performance. The proposed model has flexibility in changing the number of filters in convolution units. By taking advantage of the strengths of residual and recurrent structures in terms of reuse of convolutional functions, the new model, therefore, was not only smaller in size but also has superior performance compared to the traditional Unet model and the others. The evaluations were performed on three public Colonoscopy image datasets: CVC-ClinicDB, ETIS-LaribPolypDB, and CVC-ColonDB. The Dice score on CVC-ClinicDB reached 94.59%, ETIS-LaribPolypDB reached 92.73% and 93.31% on CVC-ColonDB dataset. The experimental results obtained from the proposed network on datasets were better than those in recent related studies. The introduced model has a smaller size than the traditional model nevertheless has outstanding performance, therefore, it would be extremely productive for developing applications on low-performance devices.

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An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

Cited by 12 publications

References 19 publications

ContourNet—An Automated Segmentation Framework for Detection of Colonic Polyps

ContourNet—An Automated Segmentation Framework for Detection of Colonic Polyps

Graphical Image Region Extraction with K-Means Clustering and Watershed

Automatic Polyp Segmentation Using Modified Recurrent Residual Unet Network

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