We propose a new system for automatic image segmentation of organs of the human pelvic area. The algorithm is based on multi-region growing followed by a watershed transform. The main contributions are the method of selecting seed pixels and predicating the multiregion growing algorithm, and the segmentation results achieved. The system was tested in 400 images and its efficiency was measured by two different statistical methods - correlation and the t-test. The clinical application of the proposed technique is radiotherapy treatment, which depends on the delineation of the treated area. The organs of interest are the rectum, bladder and seminal vesicles.

This paper proposes a new representation for color texture using a set of multiscale illuminant invariant features. The approach was specifically developed to investigate the feasibility of using machine vision to automatically monitor populations of animal species in the Amazon Forest. The approach uses a combination of Finlayson's (1996) color angle idea and Gabor multichannel filters. Using a database of color textures from species of Amazonian monkey, and also a previously published reference database of color regions, we show that the approach performs better than methods based on color angles or Gabor filters alone. The Monkey database was compiled from texture segments extracted from a video of the Amazon Forest using a spatial-temporal segmentation algorithm. The approach is evaluated by applying two classification tests in order to measure the quality of the recognition features: root mean square (RMS) analysis and receiver operating characteristic (ROC) analysis.

This paper proposes a new representation for color texture using a set of multiscale illuminant invariant features. The approach was specifically developed to investigate the feasibility of using machine vision to automatically monitor populations of animal species in ecologically sensitive regions, such as the Amazon Forest. The approach uses a combination of Finlayson's (1994) color angle idea and Gabor multichannel filters and was inspired by the multichannel model of the human visual system (HVS). Using a database of color textures from three species of Amazonian monkey, and also a previously published reference database of color regions, we show that the approach performs better than methods based on color angles or Gabor filters alone. The Monkey database was compiled from texture segments extracted from a video of the Amazon Forest using a spatio-temporal segmentation algorithm. The approach is evaluated by applying two different classification tests in order to measure the quality of the recognition features root mean square (RMS) analysis and receiver operating characteristic (ROC) analysis.