Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry

Bráz, Geraldo; Rocha, Simara Vieira da; Almeida, João Dallyson Sousa de; Paiva, Anselmo Cardoso de; Silva, Aristófanes Corrêa; Gattass, Marcelo

doi:10.1007/s11042-018-6259-z

Cited by 22 publications

(20 citation statements)

References 37 publications

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“…Compared with traditional breast mass classification methods, deep learning can automatically extract discriminative features from mammographic images and avoid the problem of poor discriminative ability of features using the manual design feature extraction method. We cited the experimental results in the works of literatures for Eltonsy [ 26 ], Sampat [ 37 ], Wu [ 38 ], Junior [ 39 ], Liu [ 40 ] and Cao [ 41 ] in Table 1 . RetinaNet [ 42 ], FSAF [ 43 ], Foveabox [ 44 ] and our method in Table 1 are evaluated on the subset used in this paper.…”

Section: Methodsmentioning

confidence: 99%

Breast Mass Detection in Mammography Based on Image Template Matching and CNN

Sun

Wang

et al. 2021

Sensors

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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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Section: Methodsmentioning

confidence: 99%

Breast Mass Detection in Mammography Based on Image Template Matching and CNN

Sun

Wang

et al. 2021

Sensors

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“…Breast cancer remains a leading cause of cancer deaths among women in many parts of the world [1]. It is identified as a chronic disease, contributing to female mortality across the globe [2]. According to recent reports, an estimated 268,600 new cases of invasive breast cancer are to be diagnosed in women in the US during 2019, in addition to 2,670 new cases to be diagnosed in men.…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing demand for accurate and fast computing algorithms to segment the diseased regions from the mammogram [2]. Still, finding an accurate and efficient breast region segmentation technique remains a challenging problem in digital mammography [4].…”

Section: Introductionmentioning

confidence: 99%

“…Junior G. et al [2] proposed a method for the detection of mass regions on digitized mammograms though diversity analysis, geostatistical and concave geometry (Alpha Shapes). The detection rate for each feature extraction were evaluated using Support Vector Machine in both MIAS and DDSM databases, with 74 and 621 mammograms, respectively, all containing at least one mass region.…”

Section: Introductionmentioning

confidence: 99%

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Detection of partially overlapped masses in mammograms

Alhabib

2020

IJEECS

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<span>Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis.</span>

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“…The breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry is studied in [5]. These features are evaluated using Support Vector Machine in MIAS and DDSM database, with 74 and 621 mammograms, respectively.…”

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confidence: 99%

Guest Editorial: Advances in Computational Intelligence for Multimodal Biomedical Imaging

Jennane

Hamida

Benali

et al. 2019

Multimed Tools Appl

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Nowadays, many modalities such as CT, X-ray scanners, MRI/fMRI, PET scan, etc. generate complex images with a large amount of data that are becoming extremely difficult to handle. This growing mass of data requires new strategies for the diagnosis of diseases and new therapies. In recent years, particular attention has been paid to computational intelligence methods in multimodal biomedical imaging applications. Inspired by artificial intelligence, mathematics, biology and other fields, these methods can find relationships between different categories of this complex data and provide a set of tools for the diagnosis and monitoring of the disease. This special issue provides a forum to publish original research papers covering the state-ofthe-art, new algorithms, methodologies, theories and implementations of computational intelligence methods for computer-aided diagnostic systems and multimodal biomedical imaging applications such as classification, restoration and registration.

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Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry

Cited by 22 publications

References 37 publications

Breast Mass Detection in Mammography Based on Image Template Matching and CNN

Breast Mass Detection in Mammography Based on Image Template Matching and CNN

Detection of partially overlapped masses in mammograms

Guest Editorial: Advances in Computational Intelligence for Multimodal Biomedical Imaging

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