Multidimensional Wavelet Analysis for Recognition of Lesions in Colposcopy Test

Abstract

Cervical cancer is an important worldwide disease due the high rate of incidence in the population. Colposcopy is one of the diagnostic tests employed in recognition of lesions, which performs a visual examination of the cervix based on temporal reaction of the surface stained with acetic acid. It is proposed in this paper to evaluate the temporal texture changes produced by the acetic acid based on the concept of the wavelet-aggregated signal in order to identify lesions. An aggregated signal is a scalar signal providing maximum information on the most general variations present in all the processes analyzed and at the same time suppressing components that are characteristic of individual processes. Texture metrics based on spatial information are used in order to analyze temporally the acetic acid response and deduce appropriate signatures. Later, temporal information is analyzed using multidimensional wavelet analysis for identification of lesions.