Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE

Sajeev, Shelda; Bajger, Mariusz

doi:10.1109/dicta.2015.7371305

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…CLAHE is a method to improve the low contrast problem of digital images. It has been shown that CLAHE is well suited for biomedical images such as mammograms, where it can improve image quality by removing noise [29][30][31]. Therefore, in this paper, CLAHE is introduced into the preprocessing stage of data to preprocess the data and improve the image's contrast.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Tuberculosis Detection Method Using Attention and Sparse R-CNN

2022

KSII TIIS

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To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest Xray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

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Section: Data Preprocessingmentioning

confidence: 99%

A Tuberculosis Detection Method Using Attention and Sparse R-CNN

2022

KSII TIIS

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“…For example, it can make us distinguish interesting objects from improved backgrounds. Besides, CE is often adopted as a pre-processing step for many computer vision problems including object recognition [1], [2], object-ofinterest image segmentation [3], [4] medical images enhancement to detect and interpret diseases [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Ramp Distribution-Based Contrast Enhancement Techniques and Over-Contrast Measure

Lee

Kim

2019

IEEE Access

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Our approach to contrast enhancement (CE) of images is based on natural scene statistics (NSS). We show, in this paper, that the average intensity distribution of natural images can be linearly approximated to the ramp distribution in an ordered histogram domain as the contrast increases. Based on this finding, we propose ramp distribution-based global and local CE algorithms. The ramp distribution-based slant thresholding (RDST) algorithm is proposed as a global CE method which uses slant thresholding in an ordered histogram domain to yield a contrast-enhanced image. Also, the ramp distributionbased adaptive slant thresholding (RDAST) algorithm is proposed as a local CE method. It adaptively adjusts a slant angle of the ramp distribution in each block to suppress noise amplification in uniform regions and maximizes contrast in non-uniform regions. The RDAST also employs a scaled global modified histogram to minimize sensitivity to block size changes. Moreover, we propose a metric to measure the amount of over-contrast in an image to evaluate all CE algorithms more correctly. The experimental results show that the proposed algorithms have better or competitive performance as well as computational efficiency. INDEX TERMS Image enhancement, contrast enhancement, ramp distribution, over-contrast measure.

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“…Segmentation of masses located in dense background is a challenging task because of the overlapped normal dense tissues and obscured mass boundaries [10, 11]. A few studies have attempted to improve mass segmentation in dense background [12, 13], but their effectiveness in mass classification/detection has not been demonstrated yet. Other commonly used approaches like grey‐level co‐occurence matrix (GLCM) features and HOG features may also fail to identify masses localised in dense background [14].…”

Section: Introductionmentioning

confidence: 99%

Superpixel texture analysis for classification of breast masses in dense background

Sajeev

Bajger

2018

IET Computer Vision

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Finding masses in dense background is a difficult task for even experienced radiologist. It is due to the similarity of intensity between the masses and the overlapped normal dense tissues. A novel method for classification of masses localised in dense background of breast is proposed. Nine structured superpixel patterns were generated using local binary pattern technique on superpixels. Analysis of these nine structured superpixel patterns revealed the most prominent ones, allowing for successful classification of malignant masses and normal dense breast regions. Two mammographic databases were used to evaluate the proposed approach: the publicly available digital database for screening mammography (DDSM), and a local database of mammograms (BreastScreen SA, BSSA). A total of 525 regions of interest (ROIs) were used (301 extracted from DDSM and 224 from BSSA). All 525 ROIs were localised in dense backgrounds of breasts. The results indicate that features generated from structured superpixel patterns can produce very effective and efficient texture descriptors of breast masses localised in dense background. Using Fisher linear discriminant analysis classifier, an area under the receiver operating characteristic curve score of 0.96 was achieved for DDSM and 0.93 for BSSA with only six features.

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Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE

Cited by 12 publications

References 29 publications

A Tuberculosis Detection Method Using Attention and Sparse R-CNN

A Tuberculosis Detection Method Using Attention and Sparse R-CNN

Ramp Distribution-Based Contrast Enhancement Techniques and Over-Contrast Measure

Superpixel texture analysis for classification of breast masses in dense background

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