A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows

Nemoto, Mitsutaka; Honmura, Soshi; Shimizu, A.; Furukawa, Daisuke; Kobatake, Hidefumi; Nakamura, Shigeru

doi:10.1007/s11548-008-0267-9

Cited by 30 publications

(12 citation statements)

References 28 publications

(29 reference statements)

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“…The basic characteristic of A D s that most methods try to exploit is the existence of radiating spicules. A method based on line extraction has been presented (Nemoto et al, ), where a likelihood of A D association is based on density and geometric measures on the lines extracted. Furthermore, a number of methods based on the analysis of oriented structures, derived mainly through Gabor filters, have been reported (Karssemeijer & te Brake, ; Ayres & Rangayyan, ; Banik, Rangayyan, & Desautels, ; Rangayyan, Banik, Chakraborty, Mukhopadhyay, & Desautels, ).…”

Section: Introductionmentioning

confidence: 99%

“…The methods described so far, can be categorized as either performing AD detection (Tourassi et al, 2006;Guo et al, 2009;Wajid & Hussain, 2015) or AD identification (Karssemeijer & te Brake, 1996;Nemoto et al, 2009;Ayres & Rangayyan, 2007;Banik et al, 2013;Rangayyan et al, 2013;Biswas & Mukherjee, 2011;Beheshti et al, 2016). In the detection case, methods receive whole mammograms as inputs and attempt to define the existence of ADs.…”

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confidence: 99%

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Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework

2018

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A method based on the combination of Local Binary Pattern operator and radial lengths is presented aiming at the identification of Architectural Distortions (ADs) in mammograms. Local Binary Pattern operator, a number of its variants, and radial lengths are combined together producing a high‐dimensional feature space. A process, based on the combination of Principal Component Analysis and ttest, is used to effectively transform feature space and reveal the most descriptive features. The classification step is performed using a Support Vector Machine classifier. Open access databases (Mammographic Image Analysis Society and Digital Database for Screening Mammography) are used through an exhaustive evaluation framework that aims at eliminating both mammogram selection bias and limited subtlety variation, thus enabling a fair and complete comparison procedure. Furthermore, in order to provide a test bed for future comparisons, a dataset is constructed from all the available AD Regions Of Interest in Digital Database for Screening Mammography (163 AD vs 375 Regions Of Interest from specific normal cases) and is used to further evaluate the performance of the proposed method. The method performed flawlessly and classified correctly all cases.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework

2018

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“…With coarse texture scales, the difference in uniformity was less marked, possibly reflecting difficulty in distinguishing larger clusters of fine calcifications from larger single calcifications following image filtration at this scale. The filter we employed was non-directional (isotropic) and therefore insensitive to cluster shape (e.g., architectural distortion detection technique via line extraction as described by Nemoto et al, 2009). Since patients with DCIS may have branching calcifications or multiple clusters, often close to each other, further developing our methodology by using a directional filter could potentially improve differentiation of DCIS and IC.…”

Section: Discussionmentioning

confidence: 99%

Heterogeneity of Focal Breast Lesions and Surrounding Tissue Assessed by Mammographic Texture Analysis: Preliminary Evidence of an Association with Tumor Invasion and Estrogen Receptor Status

Ganeshan¹,

Strukowska²,

Skogen³

et al. 2011

Front. Oncol.

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Aim: This pilot study investigates whether heterogeneity in focal breast lesions and surrounding tissue assessed on mammography is potentially related to cancer invasion and hormone receptor status. Materials and Methods: Texture analysis (TA) assessed the heterogeneity of focal lesions and their surrounding tissues in digitized mammograms from 11 patients randomly selected from an imaging archive [ductal carcinoma in situ (DCIS) only, n = 4; invasive carcinoma (IC) with DCIS, n = 3; IC only, n = 4]. TA utilized band-pass image filtration to highlight image features at different spatial frequencies (filter values: 1.0–2.5) from fine to coarse texture. The distribution of features in the derived images was quantified using uniformity. Results: Significant differences in uniformity were observed between patient groups for all filter values. With medium scale filtration (filter value = 1.5) pure DCIS was more uniform (median = 0.281) than either DCIS with IC (median = 0.246, p = 0.0102) or IC (median = 0.249, p = 0.0021). Lesions with high levels of estrogen receptor expression were more uniform, most notably with coarse filtration (filter values 2.0 and 2.5, rs = 0.812, p = 0.002). Comparison of uniformity values in focal lesions and surrounding tissue showed significant differences between DCIS with or without IC versus IC (p = 0.0009). Conclusion: This pilot study shows the potential for computer-based assessments of heterogeneity within focal mammographic lesions and surrounding tissue to identify adverse pathological features in mammographic lesions. The technique warrants further investigation as a possible adjunct to existing computer aided diagnosis systems.

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“…Although significant progress has been made over the last 20 years, much work still needs to be done to improve the accuracy of the CAD systems. Even though several methods of tissue identification are available in literature, not a single method is applicable for identification of all the signs of breast cancer (Tiedeu et al 2012;Wei et al 2005;Oliver et al 2012;Matsubara et al 2000;Nemoto et al 2009). Hence,we proposed the combination of features to detect microcalcification, masses and architectural distortion.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for classification of abnormalities in digitized mammograms

Shanthi¹,

Bhaskaran²

2014

Sadhana

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Feature extraction is an important process for the overall system performance in classification. The objective of this article is to reveal the effectiveness of texture feature analysis for detecting the abnormalities in digitized mammograms using Self Adaptive Resource Allocation Network (SRAN) classifier. Thus, we proposed a feature set based on Gabor filters, fractal analysis, multiscale surrounding region dependence method (MSRDM) to identify the most common appearance of breast cancer namely microcalcification, masses and architectural distortion. The results of the experiments indicate that the proposed features with SRAN classifier can improve the classification performance. The SRAN classifier produces the classification accuracy of 98.44% for the proposed features with 192 images from MIAS dataset.

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A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows

Abstract: Combination of the likelihood of lines with point convergence index is effective in extracting architectural distortion with radiating spiculations.

Cited by 30 publications

References 28 publications

Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework

Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework

Heterogeneity of Focal Breast Lesions and Surrounding Tissue Assessed by Mammographic Texture Analysis: Preliminary Evidence of an Association with Tumor Invasion and Estrogen Receptor Status

A novel approach for classification of abnormalities in digitized mammograms

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