Classification of histological images of the endometrium using texture features.

OBJECTIVE: To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium. STUDY DESIGN: A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features. RESULTS: The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%. CONCLUSION: The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.

Classification of hysteroscopical images using texture and vessel descriptors.

In recent years, hysteroscopy, used as an outpatient office procedure, in combination with endometrial biopsy, has demonstrated its great potential as the method of first choice in the diagnosis of various gynecological abnormalities including abnormal uterine bleeding (AUB) and endometrial cancer (CA). In patients suffering with AUB, the blood vessels of the endometrium are hypertrophic, whereas in the case of CA vascularization is irregular or anarchic. In this paper, a methodology for the classification of hysteroscopical images of endometrium using vessel and texture features is presented. A total of 28 patients with abnormal uterine bleeding, 10 patients with endometrial cancer and 39 subjects with no pathological condition were imaged. 16 of the patients with AUB were premenopausal and 12 postmenopausal, all with CA were postmenopausal, and all with no pathological condition were premenopausal. All images were examined for the appearance of endometrial vessels and non-vascular structures. For each image, 167 texture and vessel's features were initially extracted, which were reduced after feature selection in only 4 features. The images were classified into three categories using artificial neural networks and the reported classification accuracy was 91.2 %, while the specificity and sensitivity were 83.8 and 93.6 % respectively.