Textural characterization of histopathological images for oral sub-mucous fibrosis detection

Krishnan, M. Muthu Rama; Shah, Pratik; Choudhary, Anirudh; Chakraborty, Chandan; Paul, Ranjan Rashmi; Ray, Ajoy Kumar

doi:10.1016/j.tice.2011.06.005

Cited by 38 publications

(16 citation statements)

References 35 publications

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“…They too had achieved 100% classification accuracy using YCbCr colour space. Some researchers have developed computer‐assisted screening system to improve the classification accuracy of submucous fibrosis (Muthu et al ., ). Muthu Rama Krishnan et al .…”

Section: Resultsmentioning

confidence: 95%

“…They too had achieved 100% classification accuracy using YCbCr colour space. Some researchers have developed computer-assisted screening system to improve the classification accuracy of submucous fibrosis (Muthu et al, 2011). Muthu Rama Krishnan et al has used five different methods (Gabor-wavelet, fractal dimension, local binary pattern, various wavelet families and Brownian motion curve) to extract features.…”

Section: Resultsmentioning

confidence: 99%

“…The SVM determines a separating hyperplane, which distinguishes positive data from negative data (Acharya et al, 2010). It is based on the idea of margin maximisation (Muthu et al, 2011). The SVM classifier (Huang & Kecman, 2005;Muthu et al, 2012a;Mandal, 2014) can be determined by solving the equation below.…”

Section: Classificationmentioning

confidence: 99%

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Textural pattern classification for oral squamous cell carcinoma

Rahman

Mahanta

Chakraborty

et al. 2017

Journal of Microscopy

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Summary Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer‐aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey‐level co‐occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

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Textural pattern classification for oral squamous cell carcinoma

Rahman

Mahanta

Chakraborty

et al. 2017

Journal of Microscopy

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“…In other words, fractal dimension is the ratio that provides a statistical index of complexity. It is calculated by comparing how the fractal pattern changes with measurement scale (Muthu Rama Krishnan et al, 2011).…”

Section: Fractal Dimension (Fd)mentioning

confidence: 99%

Structural and textural classification of erythrocytes in anaemic cases: A scanning electron microscopic study

Bhowmick

Das

Maiti

et al. 2013

Micron

Self Cite

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“…In other words, each image can be described by the distribution of words. Compared with SIFT, Gabor feature is more appropriate for characterizing the texture variation of pathological image [5]. More importantly, the performance of BoF model is often influenced by the synonyms of words and thus the BoF representation is likely to be not effective.…”

Section: Introductionmentioning

confidence: 99%

PLSA-based pathological image retrieval for breast cancer with color deconvolution

Shi

Jiang

et al. 2013

SPIE Proceedings

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Digital pathological image retrieval plays an important role in computer-aided diagnosis for breast cancer. The retrieval results of an unknown pathological image, which are generally previous cases with diagnostic information, can provide doctors with assistance and reference. In this paper, we develop a novel pathological image retrieval method for breast cancer, which is based on stain component and probabilistic latent semantic analysis (pLSA) model. Specifically, the method firstly utilizes color deconvolution to gain the representation of different stain components for cell nuclei and cytoplasm, and then block Gabor features are conducted on cell nuclei, which is used to construct the codebook. Furthermore, the connection between the words of the codebook and the latent topics among images are modeled by pLSA. Therefore, each image can be represented by the topics and also the high-level semantic concepts of image can be described. Experiments on the pathological image database for breast cancer demonstrate the effectiveness of our method.

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Textural characterization of histopathological images for oral sub-mucous fibrosis detection

Cited by 38 publications

References 35 publications

Textural pattern classification for oral squamous cell carcinoma

Textural pattern classification for oral squamous cell carcinoma

Structural and textural classification of erythrocytes in anaemic cases: A scanning electron microscopic study

PLSA-based pathological image retrieval for breast cancer with color deconvolution

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