Document Type: Original Research

Authors

1 Dept. of Pathology, Urmia University of Medical Sciences, Urmia, Iran

2 Dept. of Pathology, Isfahan Legal Medicine Organization, Isfahan, Iran

3 Dept. of Immunology, Urmia University of Medical Sciences, Urmia, Iran

4 Dept. of Public Health, Urmia University of Medical Sciences, Urmia, Iran

5 Dept. of Immunology, Tarbiat Modares University, Tehran, Iran

Abstract

  Background & Objectives: Cancer is a serious problem for human being and is becoming a serious problem day-by-day .A prerequisite for any therapeutic modality is early diagnosis. Automated cancer diagnosis by automatic image feature extraction procedures can be used as a feature extraction in the field of fractal dimension. The aim of this survey was to introduce a quantitative and objective mathematical method for pinpointing the differences between malignant and non –malignant epithelial cells in urine cytology by the use of software analysis. Materials & Methods:  Forty-one positive urine cytology and 33 negative subjects from Pathology Department of Imam Khomeini Hospital, Urmia, Iran (2003-2007) were selected at random. Digitalized images were prepared by the use of objective 100X (a digital video head) which subsequently were processed by the BeonitTM software version 1.3 (Tru Soft International inc. USA) to measure fractal dimension of nuclear boundaries. Results:  Findings revealed statistically significant differences between fractal dimensions of nuclear boundaries of cancerous and non-cancerous smears (P=0.001). Study had selected a cut-off point to (1.732 ± 0.006) to discriminate malignant and non-malignant epithelial cells in urinary smears. Conclusion: Based on diagnostic accuracy measures (sensitivity and specificity), probability of disease measures (predictive value of a positive and negative test results), and likelihood ratio of positive and negative tests, it seems fractal dimension of nuclear cell boundaries for urinary smears can be used as a feature extraction in the field of automated cancer diagnosis.  

Keywords