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White B, Harrow A, Cinelli C, Batchelder K, Khalil A. WAVELET-BASED AUTOMATIC PECTORAL MUSCLE SEGMENTATION FOR MAMMOGRAMS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.09.24302580. [PMID: 38370747 PMCID: PMC10871448 DOI: 10.1101/2024.02.09.24302580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The computational analysis to assist radiologists in the interpretation of mammograms usually requires a pre-processing step where the image is converted into a black and white mask to separate breast tissue from the pectoral muscle and the image background. The manual delineation of the breast tissue from the mammogram image is subjective and time-consuming. The 2D Wavelet Transform Modulus Maxima (WTMM) segmentation method, a powerful and versatile multi-scale edge detection approach, is adapted and presented as a novel automated breast tissue segmentation method. The algorithm computes the local maxima of the modulus of the continuous Gaussian wavelet transform to produce candidate edge detection lines called maxima chains. These maxima chains from multiple wavelet scales are optimally sorted to produce a breast tissue segmentation mask. The mammographic mask is quantitatively compared to a manual delineation using the Dice-Sorenson Coefficient (DSC). The adaptation of the 2D WTMM segmentation method produces a median DSC of 0.9763 on 1042 mediolateral oblique (MLO) 2D Full Field Digital mammographic views from 82 patients obtained from the MaineHealth Biobank (Scarborough, Maine, USA). Our proposed approach is evaluated against OpenBreast , an open-source automated analysis software in MATLAB, through comparing each approach's masks to the manual delineations. OpenBreast produces a lower median DSC of 0.9710. To determine statistical significance, the analysis is restricted to 82 mammograms (one randomly chosen per patient), which yields DSC medians of 0.9756 for the WTMM approach vs. 0.9698 for OpenBreast ( p -value = 0.0067 using a paired Wilcoxon Rank Sum test). Thus, the 2D WTMM segmentation method can reliably delineate the pectoral muscle and produce an accurate segmentation of whole breast tissue in mammograms.
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Si T, Patra DK, Mallik S, Bandyopadhyay A, Sarkar A, Qin H. Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images. Sci Rep 2023; 13:11577. [PMID: 37463919 PMCID: PMC10354050 DOI: 10.1038/s41598-023-36300-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/31/2023] [Indexed: 07/20/2023] Open
Abstract
Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur's entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients' T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved [Formula: see text], [Formula: see text], and [Formula: see text] respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of [Formula: see text] , sensitivity of [Formula: see text] , and DSC of [Formula: see text]. The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, [Formula: see text]-score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.
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Affiliation(s)
- Tapas Si
- Department of Computer Science & Engineering, University of Engineering & Management, Jaipur, GURUKUL, Sikar Road (NH-11), Udaipuria Mod, Jaipur, Rajasthan, 303807, India
| | - Dipak Kumar Patra
- Department of Computer Science, Hijli College, Kharagpur, West Bengal, 721306, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - Anjan Bandyopadhyay
- School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India
| | - Achyuth Sarkar
- Department of Computer Science & Engineering, National Institute of Technology Arunachal Pradesh, Arunachal Pradesh, 791113, India
| | - Hong Qin
- Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN, USA.
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Tammineedi VSV, C. R, D. GK, Yalla V. Improvement of Segmentation Efficiency in Mammogram Images Using Dual-ROI Method. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2022. [DOI: 10.4018/ijhisi.305236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
Mammogram segmentation utilizing multi-region of intrigue is a standout amongst the most rising exploration territory in the medical image analysis. The steps engaged with the research are grouped into two kinds: 1) segmentation of mammogram images and 2) extraction of texture features from mammogram images. To overcome these difficulties, a compelling technique is proposed in this paper that comprises of three phases. In the principal arrangement, mammogram images from INbreast database are selected and improved utilizing Laplacian filtering. At that point, the pre-processed mammogram images are utilized for segmentation utilizing modified adaptively regularized kernel-based fuzzy C means (M-ARKFCM). After segmentation, statistical texture FE is connected for recognizing the patterns of cancer and non-cancer regions in mammogram images. Finally, the experimental outcome demonstrated that the proposed approach enhanced the segmentation efficiency by methods of statistical parameters contrasted with the existing operating procedures.
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Affiliation(s)
| | - Raju C.
- Sri Venkateswara Engineering College, India
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Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning. Sci Rep 2022; 12:12060. [PMID: 35835933 PMCID: PMC9283472 DOI: 10.1038/s41598-022-16141-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/05/2022] [Indexed: 12/02/2022] Open
Abstract
Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning architecture that automatically estimates the area-based breast percentage density from mammograms using a weight-adaptive multitask learning approach. The proposed approach simultaneously segments the breast and dense tissues and further estimates the breast percentage density. We evaluate the performance of the proposed model in both segmentation and density estimation on an independent evaluation set of 7500 craniocaudal and mediolateral oblique-view mammograms from Kuopio University Hospital, Finland. The proposed multitask segmentation approach outperforms and achieves average relative improvements of 2.88% and 9.78% in terms of F-score compared to the multitask U-net and a fully convolutional neural network, respectively. The estimated breast density values using our approach strongly correlate with radiologists’ assessments with a Pearson’s correlation of \documentclass[12pt]{minimal}
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\begin{document}$$r = 0.90$$\end{document}r=0.90 (95% confidence interval [0.89, 0.91]). We conclude that our approach greatly improves the segmentation accuracy of the breast area and dense tissues; thus, it can play a vital role in accurately computing the breast density. Our density estimation model considerably reduces the time and effort needed to estimate density values from mammograms by radiologists and therefore, decreases inter- and intra-reader variability.
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Rahman MA, Jha RK. Multidirectional Gabor Filter-Based Approach for Pectoral Muscle Boundary Detection. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3058157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8576768. [PMID: 35083334 PMCID: PMC8786533 DOI: 10.1155/2022/8576768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/26/2021] [Accepted: 12/17/2021] [Indexed: 11/18/2022]
Abstract
In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.
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Maghsoudi OH, Gastounioti A, Scott C, Pantalone L, Wu FF, Cohen EA, Winham S, Conant EF, Vachon C, Kontos D. Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment. Med Image Anal 2021; 73:102138. [PMID: 34274690 PMCID: PMC8453099 DOI: 10.1016/j.media.2021.102138] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/29/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023]
Abstract
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.
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Affiliation(s)
- Omid Haji Maghsoudi
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
| | - Aimilia Gastounioti
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Lauren Pantalone
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Fang-Fang Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Eric A. Cohen
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Stacey Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Emily F. Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
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Fan T, Wang G, Li Y, Wang Z, Wang H. A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape
and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within
a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally, we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional
information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy
functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than
other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.
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Affiliation(s)
- Tongle Fan
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Guanglei Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Yan Li
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Zhongyang Wang
- Affiliated Hospital of Hebei University, Banding, Hebei 071002, China
| | - Hongrui Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
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9
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Breast Cancer Segmentation Methods: Current Status and Future Potentials. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9962109. [PMID: 34337066 PMCID: PMC8321730 DOI: 10.1155/2021/9962109] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022]
Abstract
Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
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Pawar SD, Sharma KK, Sapate SG, Yadav GY. Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.08.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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11
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Stephan P, Stephan T, Kannan R, Abraham A. A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05997-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Zahoor S, Lali IU, Khan MA, Javed K, Mehmood W. Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review. Curr Med Imaging 2021; 16:1187-1200. [PMID: 32250226 DOI: 10.2174/1573405616666200406110547] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/25/2019] [Accepted: 01/03/2020] [Indexed: 11/22/2022]
Abstract
Breast Cancer is a common dangerous disease for women. Around the world, many women have died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues, there are several techniques and methods. The image processing, machine learning, and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to save a women's life. To detect the breast masses, microcalcifications, and malignant cells,different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are also challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.
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Affiliation(s)
- Saliha Zahoor
- Department of Computer Science, University of Gujrat, Gujrat, Pakistan
| | - Ikram Ullah Lali
- Department of Information Technology, University of Education, Lahore, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Museum Road Taxila, Rawalpindi, Pakistan
| | - Kashif Javed
- Department of Robotics, SMME NUST, Islamabad, Pakistan
| | - Waqar Mehmood
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
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Rampun A, Morrow PJ, Scotney BW, Wang H. Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns. Comput Biol Med 2020; 122:103842. [DOI: 10.1016/j.compbiomed.2020.103842] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/02/2020] [Accepted: 06/02/2020] [Indexed: 12/01/2022]
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14
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A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artif Intell Rev 2020. [DOI: 10.1007/s10462-019-09721-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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15
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Kumar MNA, Kumar MNA, Sheshadri HS. Computer Aided Detection of Clustered Microcalcification: A Survey. Curr Med Imaging 2020; 15:132-149. [PMID: 31975660 DOI: 10.2174/1573405614666181012103750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 09/23/2018] [Accepted: 09/27/2018] [Indexed: 11/22/2022]
Abstract
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. 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.
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Affiliation(s)
- M N Arun Kumar
- Department of Computer Science and Engineering, Federal Institute of Science and Technology, Ernakulam, India
| | - M N Anil Kumar
- Department of Electronics and Communication Engineering, Federal Institute of Science and Technology, Ernakulam, India
| | - H S Sheshadri
- Department of Electronics and Communication Engineering, PES College of Engineering, Mandya, Karnataka, India
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16
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Shen R, Yan K, Xiao F, Chang J, Jiang C, Zhou K. Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection. J Digit Imaging 2019; 31:680-691. [PMID: 29582242 DOI: 10.1007/s10278-018-0068-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.
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Affiliation(s)
- Rongbo Shen
- Key Laboratory of Information Storage System, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, People's Republic of China.
| | | | | | | | | | - Ke Zhou
- Key Laboratory of Information Storage System, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, People's Republic of China
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17
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A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm. Biomed Eng Lett 2019; 9:481-496. [PMID: 31799016 DOI: 10.1007/s13534-019-00135-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/06/2019] [Accepted: 10/20/2019] [Indexed: 10/25/2022] Open
Abstract
Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.
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18
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Rampun A, López-Linares K, Morrow PJ, Scotney BW, Wang H, Ocaña IG, Maclair G, Zwiggelaar R, González Ballester MA, Macía I. Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Med Image Anal 2019; 57:1-17. [DOI: 10.1016/j.media.2019.06.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/22/2022]
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19
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Gardezi SJS, Elazab A, Lei B, Wang T. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review. J Med Internet Res 2019; 21:e14464. [PMID: 31350843 PMCID: PMC6688437 DOI: 10.2196/14464] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 06/11/2019] [Accepted: 06/12/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. OBJECTIVE This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. METHODS In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. RESULTS The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. CONCLUSIONS From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
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Affiliation(s)
- Syed Jamal Safdar Gardezi
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ahmed Elazab
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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20
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Mammogram segmentation using multi-atlas deformable registration. Comput Biol Med 2019; 110:244-253. [DOI: 10.1016/j.compbiomed.2019.06.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/17/2019] [Accepted: 06/03/2019] [Indexed: 11/20/2022]
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21
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Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. Int J Med Inform 2019; 126:26-34. [DOI: 10.1016/j.ijmedinf.2019.02.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/10/2018] [Accepted: 02/03/2019] [Indexed: 02/03/2023]
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22
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Rampun A, Scotney BW, Morrow PJ, Wang H, Winder J. Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours. Artif Intell Med 2019; 97:44-60. [DOI: 10.1016/j.artmed.2018.10.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 09/26/2018] [Accepted: 10/23/2018] [Indexed: 11/28/2022]
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23
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Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft comput 2019. [DOI: 10.1007/s00500-019-04066-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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24
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S P, N KV, S S. Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction. Asian Pac J Cancer Prev 2019; 20:157-165. [PMID: 30678427 PMCID: PMC6485576 DOI: 10.31557/apjcp.2019.20.1.157] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the
abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected
area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate
them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to
early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy
clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order
moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy
clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied
to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from
the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated
the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating
the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed
method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in
diagnosing breast cancer at an early stage.
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Affiliation(s)
- Parvathavarthini S
- Department of Computer Technology, Kongu Engineering College, Perundurai, Tamilnadu, India.
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25
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Pandey D, Yin X, Wang H, Su MY, Chen JH, Wu J, Zhang Y. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon 2018; 4:e01042. [PMID: 30582055 PMCID: PMC6299131 DOI: 10.1016/j.heliyon.2018.e01042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 11/04/2018] [Accepted: 12/10/2018] [Indexed: 12/13/2022] Open
Abstract
Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.
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Affiliation(s)
- Dinesh Pandey
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou 510006, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Min-Ying Su
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
| | - Jeon-Hor Chen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
- Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Jianlin Wu
- Department of Radiology, Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
| | - Yanchun Zhang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
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26
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Shi P, Zhong J, Rampun A, Wang H. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput Biol Med 2018; 96:178-188. [DOI: 10.1016/j.compbiomed.2018.03.011] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/19/2018] [Accepted: 03/14/2018] [Indexed: 02/03/2023]
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Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies. J Imaging 2018. [DOI: 10.3390/jimaging4010014] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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