An automated method for detecting architectural distortions on mammograms using direction analysis of linear structures

Matsubara, Tomoko; Akari, Ito; Tsunomori, Akinori; Hara, Takeshi; Muramatsu, Chisako; Endo, Tokiko; Fujita, Hiroshi

doi:10.1109/embc.2015.7318939

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…In another work, the researcher exploited the extraction of yields pixel by pixel vector maps using diffusion tensor imaging (DTI) to track the mammary architectural elements [150]. The study in [152] proposed a novel method using direction analysis of linear structures to detect AD. The determined directions (0Â • , 22.5Â • , 45Â • , 67.5Â • , 90Â • , 112.5Â • , 135Â • , and 157.5Â • ) were then used to calculate the isotropic indexes to extract suspicious areas.…”

Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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“…The parameters of the phase portrait model are used along with a classifier such as support vector machine (SVM) to distinguish between an AD site and a normal site. In [10,11], ridges are extracted with the aid of linear directional filters. The concentration (evaluating the radiating pattern of linear structures) and isotropic indices (evaluating the distribution of the orientations of the linear structures) are used to evaluate the pattern of the ridges.…”

Section: Literature Surveymentioning

confidence: 99%

Context‐based ensemble classification for the detection of architectural distortion in a digitised mammogram

Akhtar

Mukherjee

2020

IET Image Processing

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“…Yoshikawa et al proposed using an adaptive Gabor filter to detect AD, and the filter parameters were adjusted according to the mammary structure [15]. Matsubara et al proposed to extract the line structure from the mammograms by the normal curvature for detecting AD [16]. Lakshmanan et al proposed using geometrical properties of edge structures for detecting AD [17].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Dong

Sun

et al. 2019

Applied Sciences

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Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

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An automated method for detecting architectural distortions on mammograms using direction analysis of linear structures

Cited by 11 publications

References 7 publications

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Context‐based ensemble classification for the detection of architectural distortion in a digitised mammogram

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

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