Computer Aided Detection of Clustered Microcalcification: A Survey

2019 ◽  
Vol 15 (2) ◽  
pp. 132-149 ◽  
Author(s):  
M.N. Arun Kumar ◽  
M.N. Anil Kumar ◽  
H.S. Sheshadri
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Pectoral Muscle ◽  
Diagnostic Purpose ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Muscle Segmentation ◽  
Microcalcification Clusters ◽  
Visual Indication

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. </P><P> Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

2019 ◽  
Vol 53 (3) ◽  
pp. 1873-1918 ◽  
Author(s):  
Mehrdad Moghbel ◽  
Chia Yee Ooi ◽  
Nordinah Ismail ◽  
Yuan Wen Hau ◽  
Nogol Memari
Keyword(s):  
Pectoral Muscle ◽  
Computer Aided Detection ◽  
Segmentation Methods ◽  
Computer Aided ◽  
Muscle Segmentation

scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

scholarly journals Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Woong Bae Yoon ◽  
Ji Eun Oh ◽  
Eun Young Chae ◽  
Hak Hee Kim ◽  
Soo Yeul Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Automatic Segmentation ◽  
Automatic Detection ◽  
Pectoral Muscle ◽  
Accurate Information ◽  
Detection Accuracy ◽  
Pixel Intensity ◽  
Muscle Region ◽  
Computer Aided ◽  
Muscle Segmentation

The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.

Performance Evaluation of Image Processing Algorithms in CADe

2005 ◽  
Vol 4 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Michael A. Wirth
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Performance Evaluation ◽  
Cancer Screening ◽  
Ad Hoc ◽  
Evaluation Process ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Image Processing Algorithms ◽  
Processing Algorithms

One of the key limitations of existing image processing algorithms for computer-aided detection (CADe) is that they are often designed and evaluated in an ad hoc manner. This paper characterizes some of the issues and shortcomings in existing performance evaluation paradigms for image processing algorithms in breast cancer screening, particularly in the context of computer aided detection. We present the framework for establishing a performance evaluation process using standardized criteria. We conclude with some specific recommendations to improve the infrastructure for evaluation the performance of image processing algorithms.

scholarly journals The value of computer aided detection system for mammography in the fatty breast: the results of the single-centered, prospective, randomized clinical trial

2021 ◽  
Vol 25 (4) ◽  
pp. 93-105
Author(s):  
D. V. Pasynkov ◽  
M. G. Tukhbatullin ◽  
R. Sh. Khasanov
Keyword(s):  
Detection System ◽  
Low Density ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Initial Screening ◽  
Prospective Randomized Clinical Trial ◽  
Primary Screening ◽  
Targeted Ultrasound ◽  
Computer Aided

Aim. To assess the reasonability to use CAD added to mammography with subsequent targeted ultrasound (US) of CAD markings in patients with low-density (ACR A-В) breasts.Materials and methods. In the prospective study we included 2326 women with low breast density. They were randomized for CAD (MammCheck II of our own design) checking with subsequent targeted US (MMG + CAD group) or without CAD (MMG only group). After the initial screening we performed the 3-year follow-up phase.Results. Totally, during the primary screening in the MMG only group we found 77 breast cancers (BCs) (28,57% of them sized less than 1 cm), in the MMG + CAD group – 69 BCs (36,23% of them sized less than 1 cm), р > 0.05. The suspicious lesion was identified only during the targeted US of the CAD marking in 4 of 25 women in the MMG + CAD group, and all these BCs were below 1 cm in size. During the subsequent follow-up in the MMG only group we found 5 additional BCs, with no such cases in the MMG + CAD group (p < 0.05). Three of these five BCs were retrospectively marked by CAD. The only visible BC that was not marked by CAD was 3 mm in size.Discussion. The overall false positive marking rate was 0.31 и 0.28 per film-screen and digital image, respectively (р > 0.05).Conclusion. The CAD usage added to mammography with subsequent targeted US of markings in patients with low-density (ACR A-В) breast is reasonable due to the significant decrease of the BC rate diagnosed during the 3-year follow-up. This combination detected 77 of the 77 (100.00%) BCs compared to 69 of 74 (93.24%) BCs when only mammography used.

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