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Zaylaa AJ, Kourtian S. Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:2312. [PMID: 38610522 PMCID: PMC11014206 DOI: 10.3390/s24072312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.
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Affiliation(s)
- Amira J. Zaylaa
- Biomedical Engineering Program, Electrical and Computer Engineering Department, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon
| | - Sylva Kourtian
- Centre de Recherche du Centre Hospitalier, l’Université de Montréal, Montréal, QC H2X 0A9, Canada;
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Gao Y, Lin J, Zhou Y, Lin R. The application of traditional machine learning and deep learning techniques in mammography: a review. Front Oncol 2023; 13:1213045. [PMID: 37637035 PMCID: PMC10453798 DOI: 10.3389/fonc.2023.1213045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients' physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients' overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.
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Affiliation(s)
- Ying’e Gao
- School of Nursing Fujian Medical University, Fuzhou, China
| | - Jingjing Lin
- School of Nursing Fujian Medical University, Fuzhou, China
| | - Yuzhuo Zhou
- Department of Surgery, Hannover Medical School, Hannover, Germany
| | - Rongjin Lin
- School of Nursing Fujian Medical University, Fuzhou, China
- Department of Nursing, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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3
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Sarvestani ZM, Jamali J, Taghizadeh M, Dindarloo MHF. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04571-y. [PMID: 36680580 DOI: 10.1007/s00432-023-04571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy. METHODS The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work. RESULTS The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%. CONCLUSION The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
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Affiliation(s)
| | - Jasem Jamali
- Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
| | - Mehdi Taghizadeh
- Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
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Touil A, Kalti K, Conze PH, Solaiman B, Mahjoub MA. A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation. J Digit Imaging 2022; 35:1560-1575. [PMID: 35915367 PMCID: PMC9712888 DOI: 10.1007/s10278-022-00678-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 05/31/2022] [Accepted: 06/04/2022] [Indexed: 11/29/2022] Open
Abstract
In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.
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Affiliation(s)
- Asma Touil
- Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023 Tunisia
- Université de Sousse, Supérieur d’Informatique et des Techniques de Communication, Hammam Sousse, 4011 Tunisia
- IMT Atlantique, LaTIM UMR 1101, Brest, 29200 France
| | - Karim Kalti
- Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023 Tunisia
- Université de Sousse, Supérieur d’Informatique et des Techniques de Communication, Hammam Sousse, 4011 Tunisia
- University of Monastir, Computer Science Department, Faculty of Science, 5019 Monastir, Tunisia
| | | | | | - Mohamed Ali Mahjoub
- Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023 Tunisia
- Université de Sousse, Supérieur d’Informatique et des Techniques de Communication, Hammam Sousse, 4011 Tunisia
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Touil A, Kalti K, Conze PH, Solaiman B, Mahjoub MA. A new conditional region growing approach for microcalcification delineation in mammograms. Med Biol Eng Comput 2021; 59:1795-1814. [PMID: 34304371 DOI: 10.1007/s11517-021-02379-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 05/07/2021] [Indexed: 11/28/2022]
Abstract
Microcalcifications (MCs) are considered as the first indicator of breast cancer development. Their morphology, in terms of shape and size, is considered as the most important criterion that determines their malignity degrees. Therefore, the accurate delineation of MC is a cornerstone step in their automatic diagnosis process. In this paper, we propose a new conditional region growing (CRG) approach with the ability of finding the accurate MC boundaries starting from selected seed points. The starting seed points are determined based on regional maxima detection and superpixel analysis. The region growing step is controlled by a set of criteria that are adapted to MC detection in terms of contrast and shape variation. These criteria are derived from prior knowledge to characterize MCs and can be divided into two categories. The first one concerns the neighbourhood searching size. The second one deals with the analysis of gradient information and shape evolution within the growing process. In order to prove the effectiveness and the reliability in terms of MC detection and delineation, several experiments have been carried out on MCs of various types, with both qualitative and quantitative analysis. The comparison of the proposed approach with state-of-the art proves the importance of the used criteria in the context of MC delineation, towards a better management of breast cancer. Graphical Abstract Flowchart of the proposed approach.
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Affiliation(s)
- Asma Touil
- Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, 4023, Sousse, Tunisia. .,Université de Sousse, Institut Supérieur d'Informatique et des Techniques de Communication, 4011, Hammam Sousse, Tunisia. .,IMT Atlantique, LaTIM UMR 1101, UBL, Brest, 29200, France.
| | - Karim Kalti
- Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, 4023, Sousse, Tunisia
| | | | - Basel Solaiman
- IMT Atlantique, LaTIM UMR 1101, UBL, Brest, 29200, France
| | - Mohamed Ali Mahjoub
- Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, 4023, Sousse, Tunisia
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6
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Guan Y, Wang X, Li H, Zhang Z, Chen X, Siddiqui O, Nehring S, Huang X. Detecting Asymmetric Patterns and Localizing Cancers on Mammograms. PATTERNS 2020; 1. [PMID: 33073255 PMCID: PMC7566852 DOI: 10.1016/j.patter.2020.100106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
One in eight women develops invasive breast cancer in her lifetime. The frontline protection against this disease is mammography. While computer-assisted diagnosis algorithms have made great progress in generating reliable global predictions, few focus on simultaneously producing regions of interest (ROIs) for biopsy. Can we combine ROI-oriented algorithms with global classification of cancer status, which simultaneously highlight suspicious regions and optimize classification performance? Can the asymmetry of breasts be adopted in deep learning for finding lesions and classifying cancers? We answer the above questions by building deep-learning networks that identify masses and microcalcifications in paired mammograms, exclude false positives, and stepwisely improve performance of the model with asymmetric information regarding the breasts. This method achieved a co-leading place in the Digital Mammography DREAM Challenge for predicting breast cancer. We highlight here the importance of this dual-purpose process that simultaneously provides the locations of potential lesions in mammograms. A top-performing algorithm in the International DREAM Digital Mammography Challenge Shows efficacy of asymmetric information from opposite breasts in identifying cancers Integrated pixel-level localization and overall classification into the same software
Breast cancer affects one out of eight women in their lifetime. Given the importance of the need, in this work we present a region-of-interest-oriented deep-learning pipeline for detecting and locating breast cancers based on digital mammograms. It is a leading algorithm in the well-received Digital Mammography DREAM Challenge, in which computational methods were evaluated on large-scale, held-out testing sets of digital mammograms. This algorithm connects two aims: (1) determining whether a breast has cancer and (2) determining cancer-associated regions of interest. Particularly, we addressed the challenge of variation of mammogram images across different patients by pairing up the two opposite breasts to examine asymmetry, which substantially improved global classification as well as local lesion detection. We have dockerized this code, envisioning that it will be widely used in practice and as a future reference for digital mammography analysis.
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Affiliation(s)
- Yuanfang Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Lead Contact
| | - Xueqing Wang
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hongyang Li
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhenning Zhang
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Present address: AstraZeneca, 950 Wind River Lane, Gaithersburg, MD 20878, USA
| | - Xianghao Chen
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Omer Siddiqui
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sara Nehring
- Translational Research Lab of Arkansas State University and St. Bernard's Medical Center, Jonesboro, AR 72467, USA
| | - Xiuzhen Huang
- Translational Research Lab of Arkansas State University and St. Bernard's Medical Center, Jonesboro, AR 72467, USA
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7
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Automatic detection of microcalcification based on morphological operations and structural similarity indices. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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8
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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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Hernández-Capistrán J, Martínez-Carballido JF, Rosas-Romero R. False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer. J Med Syst 2018; 42:134. [PMID: 29915992 DOI: 10.1007/s10916-018-0989-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 06/07/2018] [Indexed: 10/14/2022]
Abstract
Early automatic breast cancer detection from mammograms is based on the extraction of lesions, known as microcalcifications (MCs). This paper proposes a new and simple system for microcalcification detection to assist in early breast cancer detection. This work uses the two most recognized public mammogram databases, MIAS and DDSM. We are introducing a MC detection method based on (1) Beucher gradient for detection of regions of interest (ROIs), (2) an annulus model for extraction of few and effective features from candidates to MCs, and (3) one classification stage with two different classifiers, k Nearest Neighbor (KNN) and Support Vector Machine (SVM). For dense mammograms in the MIAS database, the performance metrics achieved are sensitivity of 0.9835, false alarm rate of 0.0083, accuracy of 0.9835, and area under the ROC curve of 0.9980 with a KNN classifier. The proposed MC detection method, based on a KNN classifier, achieves, a sensitivity, false positive rate, accuracy and area under the ROC curve of 0.9813, 0.0224, 0.9795 and 0.9974 for the MIAS database; and 0.9035, 0.0439, 0.9298 and 0.9759 for the DDSM database. By slightly reducing the true positive rate the method achieves three instances with false positive rate of 0: 2 on fatty mammograms with KNN and SVM, and one on dense with SVM. The proposed method gives better results than those from state of the art literature, when the mammograms are classified in fatty, fatty-glandular, and dense.
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Affiliation(s)
- Jonathan Hernández-Capistrán
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonantzintla, 72840, Puebla, Pue, Mexico.
| | - Jorge F Martínez-Carballido
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonantzintla, 72840, Puebla, Pue, Mexico
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10
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Abstract
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.
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Affiliation(s)
- Marcin Ciecholewski
- Faculty of Mathematics and Computer Science, Jagiellonian University, ul. Łojasiewicza 6, 30-348, Kraków, Poland.
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Salazar-Licea LA, Pedraza-Ortega JC, Pastrana-Palma A, Aceves-Fernandez MA. Location of mammograms ROI's and reduction of false-positive. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:97-111. [PMID: 28391823 DOI: 10.1016/j.cmpb.2017.02.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 02/08/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE There are many work related with segmentation techniques, including nearest neighbor algorithm, fuzzy rules, morphological filters, image entropy, thresholding, machine learning, wavelet analysis, and so on. Such methods carry out the segmentation, but take a lot of processing time by modifying the content of the image or showing discern problems in homogeneous areas, and the segmentation technique is designed to work efficiently only with the techniques used. In this paper a method to segment mammograms in order to separate breast area from pectoral-muscle avoiding bright areas that produce noise and therefore reducing false-positives is presented. METHODS The proposed methodology is divided into four sections: 1) Pre-processing to acquire image and decreasing its size. 2) Improving the image quality through image thresholding and histogram equalization. 3) Localization of regions of interest (ROI) applying Scale-Invariant Feature Transform to find image's descriptors. Clustering methods were implemented to determine the best number of clusters and which of these represent the most significant breast area. Then found ROI's coordinates are compared with the position of abnormalities diagnosed by the Mammographic Image Analysis Society. 4) Microcalcifications (mcc) detection; wavelet transform is used, and to enhance its performance different high-pass filters and high-frequency emphasis filters are evaluated. Symlet wavelets: Sym8 and Sym16 were used with different decomposition level; images results from both processes are compared and only those elements in common are detected as microcalcifications. RESULTS Moreover, muscle's remnants in the corners of the regions of interest were removed using fuzzy c-means clustering. The best results in terms of sensitivity (91.27), false-positives per image (80.25), and precision (74.38) are compared with previous work. CONCLUSIONS Results shows that the breast area can be discriminated from the pectoral-muscle by avoiding to work with brightness areas that produces false positives. Moreover, because the image size is reduced the computer processing time will be decreased. This segmentation stage can be an addition to mammograms analysis broadly, not only to find mcc but abnormalities such as circumscribed masses, speculated masses and architectural distortion. Also is useful to create automatically an unsupervised segmentation in mammograms without stage of training.
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Affiliation(s)
- Luis Antonio Salazar-Licea
- Facultad de Contaduria y Administracion, Universidad Autonoma de Queretaro, Cerro de Las Campanas S/N, Las Campanas, C.P.76010, Queretaro, Mexico.
| | - Jesús Carlos Pedraza-Ortega
- Facultad de Ingeniería, Universidad Autonoma de Queretaro, Av. de las Ciencias S/N, Juriquilla, C.P. 76230, Queretaro, Mexico
| | - Alberto Pastrana-Palma
- Facultad de Contaduria y Administracion, Universidad Autonoma de Queretaro, Cerro de Las Campanas S/N, Las Campanas, C.P.76010, Queretaro, Mexico
| | - Marco A Aceves-Fernandez
- Facultad de Ingeniería, Universidad Autonoma de Queretaro, Av. de las Ciencias S/N, Juriquilla, C.P. 76230, Queretaro, Mexico
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Plourde SM, Marin Z, Smith ZR, Toner BC, Batchelder KA, Khalil A. Computational growth model of breast microcalcification clusters in simulated mammographic environments. Comput Biol Med 2016; 76:7-13. [DOI: 10.1016/j.compbiomed.2016.06.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 05/24/2016] [Accepted: 06/20/2016] [Indexed: 01/08/2023]
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13
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Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J. A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:31-45. [PMID: 27208519 DOI: 10.1016/j.cmpb.2016.02.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 02/25/2016] [Accepted: 02/26/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Mammography analysis is an effective technology for early detection of breast cancer. Micro-calcification clusters (MCs) are a vital indicator of breast cancer, so detection of MCs plays an important role in computer aided detection (CAD) system, this paper proposes a new hybrid method to improve MCs detection rate in mammograms. METHODS The proposed method comprises three main steps: firstly, remove label and pectoral muscle adopting the largest connected region marking and region growing method, and enhance MCs using the combination of double top-hat transform and grayscale-adjustment function; secondly, remove noise and other interference information, and retain the significant information by modifying the contourlet coefficients using nonlinear function; thirdly, we use the non-linking simplified pulse-coupled neural network to detect MCs. RESULTS In our work, we choose 118 mammograms including 38 mammograms with micro-calcification clusters and 80 mammograms without micro-calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT; and we achieve the higher specificity of 94.7%, sensitivity of 96.3%, AUC of 97.0%, accuracy of 95.8%, MCC of 90.4%, MCC-PS of 61.3% and CEI of 53.5%, these promising results clearly demonstrate that the proposed approach outperforms the current state-of-the-art algorithms. In addition, this method is verified on the 20 mammograms from the People's Hospital of Gansu Province, the detection results reveal that our method can accurately detect the calcifications in clinical application. CONCLUSIONS This proposed method is simple and fast, furthermore it can achieve high detection rate, it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future.
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Affiliation(s)
- Ya'nan Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Min Dong
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Zhen Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xiaoli Gao
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Keju Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Chongfan Luo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Yide Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Jiuwen Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
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