An automatic classification system of urine bladder tumors employing morphological and textural nuclear features

IEEE International Conference on Image Processing

Volumn 2, Issue , 2001, Pages 853-856

  Spyridonos, P ^a   Ravazoula, P ^a   Cavouras, D ^a   Nikiforidis, G ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED DIAGNOSIS; IMAGE SEGMENTATION; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; MORPHOLOGY; PATTERN RECOGNITION; TREES (MATHEMATICS); TUMORS;

AUTOMATIC CLASSIFICATION SYSTEM; NUCLEAR FEATURES; URINE BLADDER TUMORS;

MEDICAL IMAGING;

EID: 0035162819     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

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9. Grading of transitional cell bladder carcinoma by texture analysis of histological sections
10. Nuclear morphometry for improved prediction of the prognosis of human bladder carcinoma
11. Classic prognostic factors, flow cytometric data, nuclear morphometric variables and mitotic indexes as predictors in transitional cell bladder cancer
12. Decorrelation methods of texture feature extraction
13. Textural features for image classification
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