Detection of architectural distortion in mammogram images using contourlet transform

Anand, S.; Rathna, R. A. V.

doi:10.1109/ice-ccn.2013.6528488

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…On the other hand, authors in [139] used Otsu technique which was performed for segmentation and then applied contoured transform and the phase portrait methods for feature extraction. In addition to image preprocessing, top-hat processing and concentration of white spaces in the sliding window also applied.…”

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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“…If more and more quantization levels are employed, the simplified filter look like HBT profile in continuous domain. Determination of quantization levels for HBT is same as in [8]. Suppose the number of uniform quantization levels is denoted as and the number of quantization levels becomes .…”

Section: Figure1 Block Diagram Proposed Mammogram Enhancementmentioning

confidence: 99%

“…However, they capture limited directional information of edges and do not perceive smoothness due to the usage of separable wavelets. Separable wavelets often have blurred regions in diagonal orientations and 2D non-separable multi-resolution filters [5][6][7][8] removes such effect. In particular, a simplified version of Gabor wavelets (SGW) for extracting the edge information for efficient ED has proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement of Mammograms and Microcalcification Detection

Anand¹,

Murugachandravel²,

Valarmathi³

et al. 2019

IJRTE

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Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.

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“…Using CT, the distribution of Mammograms (MIAS dataset) has been calculated by Anand et al [30]. Along with GLCM and morphological features, CT features have been utilized for the Mammogram image classification with the SVM method, and obtained a mean Accuracy around 100.00% by Moayedi et al [31].…”

Section: Introductionmentioning

confidence: 99%

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

2018

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Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist's decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information-for example, the Local Binary Pattern (

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Detection of architectural distortion in mammogram images using contourlet transform

Cited by 5 publications

References 14 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

Contrast Enhancement of Mammograms and Microcalcification Detection

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

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