Mammographic mass segmentation using fuzzy contours

Hmida, M. Ben; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Samia

doi:10.1016/j.cmpb.2018.07.005

Cited by 20 publications

(12 citation statements)

References 33 publications

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“…We compared the proposed algorithm with the work of K. L. Kashyap et al [8], developed a level set function for segmentation of mammogram images using a Mesh-free based radial basis function (RBF) collocation approach. A comparison is also made with the algorithm of M. Hmida et al [28], where the algorithm was a hybrid between CVM and fuzzy cmeans, they dealt with 57 mammogram images of the class of masses only. N. A. N. Azlan et al [29], used normalization and filtration as a pre-processing before segmenting the image by the active contour method.…”

Section: Resultsmentioning

confidence: 99%

Breast Tumor Detection Via Active Contour Technique

Radhi¹,

Kamil²

2021

IJIES

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The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. Segmentation algorithms often lose the path of tumor boundaries in an attempt to determine the position of them. Active contours are used widely for segmentation as a high-level technology for boundary recognition. The main aim to create a clear contrast between the tumour and the normal breast region. In this study, two approaches to active contour are applied: snakes and level sets. The proposed methods were applied to all abnormal mammogram images taken from mini-MIAS database. The first approach showed a weakness in the segmentation of this type of image, while the other approach was able to segment all the mammogram's tumours. The Chan-Vese method was the most superior of all the active contour segmentation methods. The proposed models were tested in two ways, the first is statistical the best result was for the Chan-Vese method and it came as follows (90%,95% 98%, 97%, 97%) for Jaccard, Dice, PF-Score, Precision, and Sensitivity respectively. And the other is based on the segmented region's characteristics, Chan-Vese was able to accurately determine the location and shape of the tumor. The proposed Chan-Vese approach is appropriate in adopting computer assisted detection systems to predict tumor boundaries and locations in mammography for its reliability and superior performance over other algorithms.

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Section: Resultsmentioning

confidence: 99%

Breast Tumor Detection Via Active Contour Technique

Radhi¹,

Kamil²

2021

IJIES

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“…Breast segmentation is difficult to spot due to irregularities varying from one person to another. Proper segmentation using AI greatly improves the prognosis of the patient [30]. Breast density assessment is carried out using two-dimensional mammograms [31].…”

Section: Table 1: Different Methods By Which Artificial Intelligence ...mentioning

confidence: 99%

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Dileep¹,

Gyani²

2022

Cureus

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Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.

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“…M. Hmida, et al suggested a fuzzy-energy-based model that is used for final mass delineation Where was the development of fuzzy contours and calculation of fuzzy membership maps of various groups in the image from the mini-MIAS dataset; incorporation of these maps into the CVM. The experimental findings indicate that with a precision of 88.08 per cent, the process reaches an overall true positive score of 91.12 per cent [18]. S. Soomro et al proposed a system for segmenting the inhomogeneous images by coordinating area force (local and global) term with geodesic edge term in the level set formulation, using the edge scaled method with local and global region-based statistical knowledge to validate the mammogram method images taken from the MIAS miniature dataset.…”

Section: Introductionmentioning

confidence: 90%

“…We compared the proposed algorithm with the work of A. Niaz et al [30], where they included a p-Laplace term with CVM, they tested it on only 25 mammogram images. A comparison is also made with the algorithm of M. Hmida et al [18], where the algorithm was a hybrid between CVM and fuzzy c-means, they dealt with 57 mammogram images of the class of masses only. We also provided a comparison with WACMs to prove the reliability of our work.…”

Section: Comparison With Existing Techniquesmentioning

confidence: 99%

Breast tumor segmentation in mammography image via Chan-Vese technique

Kamil

Radhi

2021

IJEECS

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The accurate segmentation of tumours is a crucial stage of diagnosis and treatment, reducing the damage that breast cancer causes, which is the most common type of cancer among women, especially after the age of forty. The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. As algorithms often lose an automatic path toward the boundaries of the tumour at try to determine the site of this type of tumour. The study aims to create a clear contrast between the tumour and the healthy breast area. For this purpose, we used a Gaussian filter as a pre-processing as it works to intensify the low-frequency components while reducing the high-frequency components as the breast structure is enhanced and noise suppression. Then, CLAHE was used to improve the contrast of the image, which increases the contrast between the tumour and the surrounding tissue and sharpens the edges of the tumour. Next, the tumour was segmented by using the Chan-Vese method with appropriate parameters defined. The proposed method was applied to all abnormal mammogram images taken from a publicly available mini-MIAS database. The proposed model was tested in two ways, the first is statistical that got results (90.1, 94.8, 95.5, 92.1, 99.5) for Jaccard, Dice, PF-Score, precision, and sensitivity respectively. And the other is based on the segmented region's characteristics that results showed the algorithm could identify the tumour with high efficiency.

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Mammographic mass segmentation using fuzzy contours

Cited by 20 publications

References 33 publications

Breast Tumor Detection Via Active Contour Technique

Breast Tumor Detection Via Active Contour Technique

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Breast tumor segmentation in mammography image via Chan-Vese technique

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