Methods for Automatic Cyst Detection and Classification in Ultrasound Images of the Female Genitalia Using Image Processing

Main Article Content

Ahmad Ali AlZubi
Hannoud Al Moghrabi
Mallak Ahmad AlZubi
Sufian Ahmad AlZubi

Keywords

Ovarian Cysts, Papillary growth, Watershed segmentation, Pre-Processing, Automatic cyst detection

Abstract

Ovarian cysts are a condition that affects female reproductive organs. Experts are able to detect ovarian cysts, which is a disorder that affects a woman's uterus, by examining the cyst's size and characteristics on an ultrasound device. Because the manual interpretation of ultrasound examination data for ovarian cyst size generally produces erroneous results, a tool is necessary to assess the size of the cyst and identify the characteristics of the cyst based on the papillary growth in the cyst. The method proposed here involves taking an ultrasound picture as its input and then running a pre-processing phase to eliminate noise before going on to a segmentation stage using the watershed approach. The last step of the process involves the extraction of individual features from the image. The findings of the segmentation are then utilised for feature extraction, namely, , to identify cysts and papillary and their diameters using contour analysis using the bounding box approach.

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