Texture descriptions and classification for pathological analysis of cancerous colonic mucosa

Esgiar, A.N.; Sharif, B.S.; Naguib, R.N.G.; Bennett, Mark K.; Murray, Andrea

doi:10.1049/cp:19990338

Cited by 9 publications

(11 citation statements)

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“…The reported classification accuracy for geometric and texture features is 80 and 90 percent, respectively. Esgiar et al improved their previous work [35] by adding image fractal dimensions to the feature set [37]. Concept of statistical scaling is used in calculating fractal dimensions, i.e., in a self-similar structure, a relationship "D f " exists between scale factor of box size and number of boxes to which structure can be divided.…”

Section: Texture Analysis-based Techniquesmentioning

confidence: 99%

“…Contrary, laser-induced fluorescence-based techniques [74], [75], [76], [77], [78], [79], [80], [81], [82], [83] have smaller accuracy, because equipment is quite delicate and a minute human error leads to wrong results. Texture [35], [36], [37], [38], [39], [40], [41], spectral [53], [54], [55], [56], [57], [58], [59], and gene analysis-based techniques [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73] show moderate accuracy. Second factor is availability of equipment.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…All the techniques [35], [36], [37], [38], [39], [40], [41] classify colon samples into normal and malignant classes but none of them distinguishes cancer grades. Data set is smaller in a few studies [35], [36], [37], [38], [40], and vice versa [39], [41]. Kalkan et al's work [39] is partially automatic, and Jiao et al's technique [40] is fully automatic.…”

Section: Comparison: Texture Analysis-based Techniquesmentioning

confidence: 99%

“…In this connection, several techniques have also been proposed for colon cancer detection. A subset of these techniques, known as textureanalysis-based techniques, have exploited the noteworthy contrast between texture of normal and malignant parts of colon biopsy images for cancer detection [35], [36], [37], [38], [39], [40], [41]. Several statistical and morphological features, which have proven to be good discriminators in the past [42], [43], have been investigated in these studies.…”

Section: Introductionmentioning

confidence: 99%

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A Recent Survey on Colon Cancer Detection Techniques

Rathore

Hussain

Ali

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.

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Section: Texture Analysis-based Techniquesmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Comparison: Texture Analysis-based Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Recent Survey on Colon Cancer Detection Techniques

Rathore

Hussain

Ali

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…For instance in [7], entropy and correlation from GLCM were obtained from normal and cancerous colonic images and used with linear discriminant function and k-nearest neighbor classifiers. GLCMs were also used in [8] to extract features from a total of 70 fluorescence microscope images of colonic tissue sections stained with a novel selective fluoroprobe.…”

Section: Implementation Of Ga-ksom and Anfis In The Classification Ofmentioning

confidence: 99%

Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images

Lim

Maguib

Dadios

et al. 2012

TENCON 2012 IEEE Region 10 Conference

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The WHO reports that colon cancer is one of the leading causes of cancer mortality in the world with the majority of people with this type of cancer belonging to those who are 60 years or older. Similar to other types of cancer, early detection is very important for a successful treatment. This paper reports on the implementation of Kohonen Self-Organizing Map (KSOM) with genetic algorithms (GA), and neuro-fuzzy classifier to classify colonic histopathological images into normal, adenomatous polyp, and cancerous. KSOM with GA, or GA-KSOM for short, was used in the feature selection stage while a neuro-fuzzy algorithm was used in the classification stage. ANFIS or Adaptive Neuro-FuzzyInference System was chosen as the structure/architecture of the neuro-fuzzy algorithm. The classification accuracies obtained were very promising with 86.7% and 87.8% for the training and testing sets, respectively.

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Ambient Diagnostics

Cai

Mick

et al. 2005

Ambient Intelligence for Scientific Discovery

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Texture descriptions and classification for pathological analysis of cancerous colonic mucosa

Cited by 9 publications

References 0 publications

A Recent Survey on Colon Cancer Detection Techniques

A Recent Survey on Colon Cancer Detection Techniques

Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images

Ambient Diagnostics

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