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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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2
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Du H, Yao MMS, Liu S, Chen L, Chan WP, Feng M. Automatic Calcification Morphology and Distribution Classification for Breast Mammograms With Multi-Task Graph Convolutional Neural Network. IEEE J Biomed Health Inform 2023; 27:3782-3793. [PMID: 37027577 DOI: 10.1109/jbhi.2023.3249404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
The morphology and distribution of microcalcifications are the most important descriptors for radiologists to diagnose breast cancer based on mammograms. However, it is very challenging and time-consuming for radiologists to characterize these descriptors manually, and there also lacks of effective and automatic solutions for this problem. We observed that the distribution and morphology descriptors are determined by the radiologists based on the spatial and visual relationships among calcifications. Thus, we hypothesize that this information can be effectively modelled by learning a relationship-aware representation using graph convolutional networks (GCNs). In this study, we propose a multi-task deep GCN method for automatic characterization of both the morphology and distribution of microcalcifications in mammograms. Our proposed method transforms morphology and distribution characterization into node and graph classification problem and learns the representations concurrently. We trained and validated the proposed method in an in-house dataset and public DDSM dataset with 195 and 583 cases,respectively. The proposed method reaches good and stable results with distribution AUC at 0.812 ± 0.043 and 0.873 ± 0.019, morphology AUC at 0.663 ± 0.016 and 0.700 ± 0.044 for both in-house and public datasets. In both datasets, our proposed method demonstrates statistically significant improvements compared to the baseline models. The performance improvements brought by our proposed multi-task mechanism can be attributed to the association between the distribution and morphology of calcifications in mammograms, which is interpretable using graphical visualizations and consistent with the definitions of descriptors in the standard BI-RADS guideline. In short, we explore, for the first time, the application of GCNs in microcalcification characterization that suggests the potential of using graph learning for more robust understanding of medical images.
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Gerbasi A, Clementi G, Corsi F, Albasini S, Malovini A, Quaglini S, Bellazzi R. DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107483. [PMID: 37030174 DOI: 10.1016/j.cmpb.2023.107483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 02/04/2023] [Accepted: 03/12/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. RESULTS The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. CONCLUSION To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.
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Affiliation(s)
- Alessia Gerbasi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
| | - Greta Clementi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Fabio Corsi
- Breast Unit, Department of Surgery, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Milan, Italy
| | - Sara Albasini
- Breast Unit, Department of Surgery, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | | | - Silvana Quaglini
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
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Marasinou C, Li B, Paige J, Omigbodun A, Nakhaei N, Hoyt A, Hsu W. Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach. J Digit Imaging 2023; 36:1016-1028. [PMID: 36820930 PMCID: PMC10287598 DOI: 10.1007/s10278-022-00751-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/04/2022] [Accepted: 12/06/2022] [Indexed: 02/24/2023] Open
Abstract
Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.
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Affiliation(s)
- Chrysostomos Marasinou
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Ste 420, Los Angeles, 90024, USA
| | - Bo Li
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, 90095, CA, USA
| | - Jeremy Paige
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, 90095, CA, USA
| | - Akinyinka Omigbodun
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Ste 420, Los Angeles, 90024, USA
| | - Noor Nakhaei
- Department of Computer Science, UCLA Samueli School of Engineering, Los Angeles, 90095, CA, USA
| | - Anne Hoyt
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, 90095, CA, USA
| | - William Hsu
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Ste 420, Los Angeles, 90024, USA.
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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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6
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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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Marathe K, Marasinou C, Li B, Nakhaei N, Li B, Elmore JG, Shapiro L, Hsu W. Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification. Comput Biol Med 2022; 146:105504. [PMID: 35525068 PMCID: PMC9839357 DOI: 10.1016/j.compbiomed.2022.105504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/07/2022] [Accepted: 04/05/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Amorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and actionable (high-risk and malignant) amorphous calcifications using a combination of local textures, global spatial relationships, and interpretable handcrafted expert features. METHOD Our approach was trained and validated on a set of 168 2D full-field digital mammography exams (248 images) from 168 patients. Within these 248 images, we identified 276 image regions with segmented amorphous calcifications and a biopsy-confirmed diagnosis. A set of local (radiomic and region measurements) and global features (distribution and expert-defined) were extracted from each image. Local features were grouped using an unsupervised k-means clustering algorithm. All global features were concatenated with clustered local features and used to train a LightGBM classifier to distinguish benign from actionable cases. RESULTS On the held-out test set of 60 images, our approach achieved a sensitivity of 100%, specificity of 35%, and a positive predictive value of 38% when the decision threshold was set to 0.4. Given that all of the images in our test set resulted in a recommendation of a biopsy, the use of our algorithm would have identified 15 images (25%) that were benign, potentially reducing the number of breast biopsies. CONCLUSIONS Quantitative analysis of full-field digital mammograms can extract subtle shape, texture, and distribution features that may help to distinguish between benign and actionable amorphous calcifications.
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Affiliation(s)
- Kalyani Marathe
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Chrysostomos Marasinou
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Beibin Li
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Noor Nakhaei
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bo Li
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Linda Shapiro
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA,Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - William Hsu
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA,Corresponding author. (W. Hsu)
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8
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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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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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Breast Mass Detection in Mammography Based on Image Template Matching and CNN. SENSORS 2021; 21:s21082855. [PMID: 33919623 PMCID: PMC8072908 DOI: 10.3390/s21082855] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/12/2021] [Accepted: 04/15/2021] [Indexed: 02/02/2023]
Abstract
In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.
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11
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Karale VA, Ebenezer JP, Chakraborty J, Singh T, Sadhu A, Khandelwal N, Mukhopadhyay S. A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms. J Digit Imaging 2020; 32:728-745. [PMID: 31388866 DOI: 10.1007/s10278-019-00249-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.
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Affiliation(s)
- Vikrant A Karale
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India
| | - Joshua P Ebenezer
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India
| | | | - Tulika Singh
- Department of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Anup Sadhu
- EKO CT & MRI Scan Center, Kolkata Medical College, Kolkata, 700004, India
| | - Niranjan Khandelwal
- Department of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Sudipta Mukhopadhyay
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
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12
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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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George M, Chen Z, Zwiggelaar R. Multiscale connected chain topological modelling for microcalcification classification. Comput Biol Med 2019; 114:103422. [PMID: 31521895 DOI: 10.1016/j.compbiomed.2019.103422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 08/29/2019] [Accepted: 08/29/2019] [Indexed: 01/26/2023]
Abstract
Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47±1.30%, 90.0±0.00%, 82.5±2.63%, 76.75±0.66% for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.
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Affiliation(s)
- Minu George
- Department of Computer Science, Aberystwyth University, SY23 3DB, UK.
| | - Zhili Chen
- School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, SY23 3DB, UK
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Basile TMA, Fanizzi A, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, Didonna V, Fausto A, Massafra R, Moschetta M, Tamborra P, Tangaro S, La Forgia D. Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system. Phys Med 2019; 64:1-9. [PMID: 31515007 DOI: 10.1016/j.ejmp.2019.05.022] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 05/08/2019] [Accepted: 05/25/2019] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.
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Affiliation(s)
- T M A Basile
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy.
| | - A Fanizzi
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - L Losurdo
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - R Bellotti
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - U Bottigli
- Department of Physical Sciences, Earth and Environment, University of Siena, Siena, Italy
| | - R Dentamaro
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - V Didonna
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - A Fausto
- Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy
| | - R Massafra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - M Moschetta
- Interdisciplinary Department of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - P Tamborra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - S Tangaro
- INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - D La Forgia
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
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Alsheh Ali M, Eriksson M, Czene K, Hall P, Humphreys K. Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation. Med Phys 2019; 46:1938-1946. [PMID: 30801718 PMCID: PMC6850331 DOI: 10.1002/mp.13450] [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] [Received: 07/22/2018] [Revised: 01/25/2019] [Accepted: 01/30/2019] [Indexed: 12/22/2022] Open
Abstract
PURPOSE We explore using the number of potential microcalcification clusters detected in for-presentation mammographic images (the images which are typically accessible to large epidemiological studies) a marker of short-term breast cancer risk. METHODS We designed a three-step algorithm for detecting potential microcalcification clusters in for-presentation digital mammograms. We studied association with short-term breast cancer risk using a nested case control design, with a mammography screening cohort as a source population. In total, 373 incident breast cancer cases (diagnosed at least 3 months after a negative screen at study entry) and 1466 matched controls were included in our study. Conditional logistic regression Wald tests were used to test for association with the presence of microcalcifications at study entry. We compared results of these analyses to those obtained using a Computer-aided Diagnosis (CAD) software (VuComp) on corresponding for-processing images (images which are used clinically, but typically not saved). RESULTS We found a moderate agreement between our measure of potential microcalcification clusters on for-presentation images and a CAD measure on for-processing images. Similar evidence of association with short-term breast cancer risk was found (P = 1 × 10 - 10 and P = 9 × 10 - 09 , for our approach on for-presentation images and for the CAD measure on for-processing images, respectively) and interestingly both measures contributed independently to association with a short-term risk (P = 9 × 10 - 03 for the CAD measure, adjusted for our proposed method and P = 1 × 10 - 04 for our proposed method, adjusted for the CAD measure). CONCLUSION Meaningful measurement of potential microcalcifications, in the context of short-term breast cancer risk assessment, is feasible for for-presentation images across a range of vendors. Our algorithm for for-presentation images performs similarly to a CAD algorithm on for-processing images, hence our algorithm can be a useful tool for research on microcalcifications and their role on breast cancer risk, based on large-scale epidemiological studies with access to for-presentation images.
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Affiliation(s)
- Maya Alsheh Ali
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
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16
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Jothilakshmi GR, Raaza A, Rajendran V, Sreenivasa Varma Y, Guru Nirmal Raj R. Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images. J Digit Imaging 2018; 31:912-922. [PMID: 29873011 DOI: 10.1007/s10278-018-0075-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.
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Affiliation(s)
| | | | - V Rajendran
- Department of ECE, Vels University, Chennai, India
| | | | - R Guru Nirmal Raj
- Department of ECE, Lakshmiammal Polytechnique College, Kovilpatti, Tamil Nadu, India
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