Pectoral Muscles Removal in Mammogram Image by Hybrid Bounding Box and Region Growing Algorithm

Saeed, Enas Mohammed Hussein; Saleh, Hayder Adnan

doi:10.1109/csase48920.2020.9142055

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Thresholding techniques are also applied to pectoral delimitation [22], [23]. Techniques such as region growth [19], [24], active contouring [25], and adaptive histogram equalization [26] among others [12], [27] are frequently used. However, some limitations in these kinds of techniques are unacceptable boundaries obtained for the under and over-segmentation.…”

Section: B Methods Based On the Removal Of The Pectoral Musclementioning

confidence: 99%

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

et al. 2023

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Section: B Methods Based On the Removal Of The Pectoral Musclementioning

confidence: 99%

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

et al. 2023

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“…Bounding Box and Region Growing approaches are used in an approach developed by Enas Mohammed Hussein Saeed et al [7] to provide more precise results. The outcome is presented in this case as the area difference between the real and bounding box, region growing, and combined areas.…”

Section: Related Workmentioning

confidence: 99%

MLO Mammogram Pectoral Masking with Ensemble of MSER and Slope Edge Detection and Extensive Pre-Processing

Ch.Sarada¹,

Lakshmi

Padmavathamma

2023

IJRITCC

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Breast Cancer is a fatal disease. Several people are losing their lives as a result of Breast Cancer. Mammography is the most often used Breast screening modality where we can see both mass and microcalcifications and both are the two major indicators of Breast Cancer. We can see Pectoral muscle also on MLO Mammograms. Digital Image Processing based computer aided diagnosis systems are being used widely to help the radiologist in detecting mass and microcalcifications in MLO Mammograms. However, because the intensity levels of the Pectoral muscle are similar to masses, in computer aided diagnosis system, Pectoral presence in the Mammogram has a detrimental effect on identifying mass. Therefore, in computer aided diagnosis system, Pectoral muscle masking substantially enhances lesion detection. This study suggests a novel ensemble computer aided diagnosis system strategy that combines the MSER based and SlopeEdgeDetection methods with extensive pre-processing to identify and cover Pectoral muscle from MLO Mammograms. The results demonstrate that the new procedure is straightforward and improves the precision of Pectoral region covering. Compared to the average accuracy of the state-of-the-art solutions which is 94%, the suggested technique achieves an accuracy of 99%. Performance analysis makes use of the Mini-MIAS database.

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“…This approach proved accurate in removing the pectoral muscle from the gray mammogram images. Algorithms like hybrid bounding box and region growing were used in [15] to remove the pectoral muscle. The maze segmentation approach used by the authors accurately obtained the best results.…”

Section: A Mammogram Preprocessingmentioning

confidence: 99%

WOMT: Wasserstein Distribution Based Minimization of False Positives in Breast Tumor Classification Using Deep Learning

Lakshmi,

Devi,

Gupta

et al. 2023

IEEE Access

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Women's life suffered and killed by invasive cancerous tumors is the most frequently highlighted header in many newsletters since 2010. There can be many. By their nature, invasive tumors spread from tissue to tissue and abduct themselves to cause new tumors. In many of the human biological parts, CT scan has given an objective approach to the early and successful detection of cancerous tumors. However, there were cases where diagnosis with CT scan images failed, resulting in many false positives. In economically backward countries, these false positives raised the notifying concern of women who cannot afford multiple diagnostic tests. Due to changes in biological metabolism, the growth of breast fat in women to considerably abnormal size is the main cause of false positives. In many of the images under study, this huge thick breast fat layer led to the rise of misclassification rate with false positives. Rendering societal help requires a precise mechanism that can reduce the false positives at an initial diagnosis. The proposed method introduces a novel constraint-based algorithm to classify a mammogram image as cancerous, aiming to reduce false positives. The proposed deep learning algorithm WOMT is trained with Wasserstein Distribution constraints that are derived from the mass transfer of cancerous patches to non-cancerous patches. The experimental simulations with a deep learning model trained with these constraints resulted in reduced false positives.

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Pectoral Muscles Removal in Mammogram Image by Hybrid Bounding Box and Region Growing Algorithm

Cited by 8 publications

References 7 publications

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

MLO Mammogram Pectoral Masking with Ensemble of MSER and Slope Edge Detection and Extensive Pre-Processing

WOMT: Wasserstein Distribution Based Minimization of False Positives in Breast Tumor Classification Using Deep Learning

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