Comparison of two AI methods for colonic tissue image classification

Filippas, John; Arochena, Hisbel; Amin, Saad; Naguib, R.N.G.; Bennett, Mark K.

doi:10.1109/iembs.2003.1279535

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Tissue diagnostics play a key role in the screening, treatment and monitoring of diseases. Large tissue image databases containing hundreds of specimens and histological types are commonly used in diagnostic services and research in the areas of tissue engineering and telemedicine (Filippas et al, 2003). Further, online databases containing tissue microarray images are now publicly available for research groups.…”

Section: Introductionmentioning

confidence: 99%

Tissue Image Classification Using Multi-Fractal Spectra

Hemsley

Mukundan

2010

International Journal of Multimedia Data Engineering and Management

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Tissue image classification is a challenging problem due to the fact that the images contain highly irregular shapes in complex spatial arrangement. The multi-fractal formalism has been found useful in characterizing the intensity distribution present in such images, as it can effectively resolve local densities and also represent various structures present in the image. This paper presents a detailed study of feature vectors derived from the distribution of Holder exponents and the geometrical characteristics of the multi-fractal spectra that can be used in applications requiring image classification and retrieval. The paper also gives the results of experimental analysis performed using a tissue image database and demonstrates the effectiveness of the proposed multi-fractal-based descriptors in tissue image classification and retrieval. Implementation aspects that need to be considered for improving classification accuracy and the feature representation capability of the proposed descriptors are also outlined.

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Section: Introductionmentioning

confidence: 99%

Tissue Image Classification Using Multi-Fractal Spectra

Hemsley

Mukundan

2010

International Journal of Multimedia Data Engineering and Management

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Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

Hilado¹,

Lim²,

Naguib³

et al. 2014

J. Adv. Comput. Intell. Intell. Inform.

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Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy.

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Comparison of two AI methods for colonic tissue image classification

Cited by 2 publications

References 6 publications

Tissue Image Classification Using Multi-Fractal Spectra

Tissue Image Classification Using Multi-Fractal Spectra

Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

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