Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust, Oliver; Acharya, U. Rajendra; Meiburger, Kristen M.; Molinari, Filippo; Koh, Joel En Wei; Yeong, Chai Hong; Kongmebhol, Pailin; Ng, Kwan Hoong

doi:10.1016/j.bbe.2018.01.001

Cited by 41 publications

(15 citation statements)

References 67 publications

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“…Because the scope of this review was limited to human populations with thyroid nodules, two studies were excluded with one being a mouse model study [30] and the other a human cell line study [31]. Two review articles and an editorial letter were excluded as they did not meet the publication inclusion criteria of original research articles [17,19,32]. A full-text review was conducted for the remaining 43 papers which resulted in the exclusion of 29 articles that did not meet the inclusion criteria.…”

Section: Resultsmentioning

confidence: 99%

“…Presently, few studies have investigated the diagnostic performance of various thyroid ultrasound CAD methods as applied in the clinical context and shown variable results. Existing systematic reviews have been based on analyzing different textural techniques and machine learning algorithms and classifiers more on a biomedical engineering perspective rather than clinical applicability [13,17,18,19]. A recent systematic review and meta-analysis study focused on the diagnostic performance of mainly the Samsung S-Detect CAD software in comparison with that of radiologists for the differentiation of thyroid nodules in clinical settings [20].…”

Section: Introductionmentioning

confidence: 99%

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The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Chambara

Ying

2019

Cancers

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Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Chambara

Ying

2019

Cancers

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“…These features are invariant to changes in rotation, contrast and luminance. These measures find applications in various domains [46,47,48].…”

Section: Figure 5 Spzm Feature Of a Sample Imagementioning

confidence: 99%

Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law’s Textures Feature Set

Mahesh¹,

Chen

Rajangam

et al. 2021

IEEE Access

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Facial expression recognition (FER) requires better descriptors to represent the face patterns as the facial region changes due to the movement of the face muscles during an expression. In this paper, a method of concatenating spatial pyramid Zernike moments based shape features and Law's texture features is proposed to uniquely capture the macro and micro details of each facial expression. The proposed method employs multilayer perceptron and radial basis function feed forward artificial neural networks for recognizing the facial expressions. The suitability of the features in recognizing the expressions is explored across the datasets independent of the subjects or persons. The experiments conducted on JAFFE and KDEF datasets demonstrate that the concatenated feature vectors are capable of representing the facial expressions with better accuracy and least errors. The radial basis function based classifier delivers a performance with an average recognition accuracy of 95.86% and 88.87% on the JAFFE and KDEF datasets respectively for subject dependent FER.

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“…He also gave drawbacks and presented examples of applications for each method. Texture analysis is widely used for various applications like medical imaging [32], remote sensing [33], or industrial automation [34]. Hence, intra-prediction results should constitute a good candidate for texture feature extraction.…”

Section: Hevc Iphm-based Classificationmentioning

confidence: 99%

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

et al. 2020

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In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing method to be decomposed into three multiscale filtered image types: Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered versions, respectively. The complete set of filtered image data constitutes the collected database. First, the images are lossless compressed using the state-of-the art High-efficiency video coding (HEVC) video coding standard. Then, the Intra-Prediction Modes Histogram (IPHM) feature descriptor is computed directly in the compressed domain from each HEVC compressed image. Finally, we apply the IPHM feature descriptors as an input of a Support Vector Machine (SVM) classifier. SVM is introduced here to strengthen the performances of the proposed classification system thanks to the powerful properties of machine learning tools. We evaluate the proposed solution we called “HEVC Multiscale Decomposition” (HEVC-MD) on a huge database of nearly 42,000 multiscale topographic images. A simple preliminary version of the algorithm reaches an accuracy of 52%. We increase this accuracy to 70% by using the multiscale analysis of the high-frequency range HP filtered image data sets. Finally, we verify that considering only the highest-scale analysis of low-frequency range LP was more appropriate for classifying our six surface topographies with an accuracy of up to 81%. To compare these new topographical descriptors to those conventionally used, SVM is applied on a set of 34 roughness parameters defined on the International Standard GPS ISO 25178 (Geometrical Product Specification), and one obtains accuracies of 38%, 52%, 65%, and 57% respectively for Sa, multiscale Sa, 34 roughness parameters, and multiscale ones. Compared to conventional roughness descriptors, the HEVC-MD descriptors increase surfaces discrimination from 65% to 81%.

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Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Cited by 41 publications

References 67 publications

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law’s Textures Feature Set

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

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