Prostate boundary segment extraction using cascaded shape regression and optimal surface detection.

In this paper, we proposed a new method (CSR+OSD) for the extraction of irregular open prostate boundaries in noisy extracorporeal ultrasound image. First, cascaded shape regression (CSR) is used to locate the position of prostate boundary in the images. In CSR, a sequence of random fern predictors are trained in a boosted regression manner, using shape-indexed features to achieve invariance against position variations of prostate boundaries.

An incremental approach to pigmented skin lesion segmentation with classification refinements in uncertain regions.

Skin lesion segmentation in dermatoscopic images is difficult because there are large inter variations in shape, size, color, and texture between lesions and skin types. Hence, computational features learned from a training set of lesion images may not be applicable to other lesion images. In this paper, we propose an incremental method for lesion segmentation.

Detection of unstained living neurospheres from phase contrast images with very large illumination variations.

Live imaging of neural stem cells and progenitors is important to follow the biology of these cells. Non-invasive imaging techniques, such as phase contrast microscopy, are preferred as neural stem cells are very sensitive to photoxic damage cause by excitation of fluorescent molecules. However, large illumination variations and weak foreground/background contrast make phase contrast images challenging for image processing.