The present invention provides a method, apparatus, and computer instructions for improved detection and display of abnormal regions in mammals such as tumors, lesions, and other abnormalities (collectively referred to as abnormalities). In a preferred embodiment, a system and process is disclosed for classification of image points in the spatial dimensions and subsequent segmentation and classification of regions using morphological descriptors that operate in up to three spatial dimensions. Additionally, mapping of a set of classification images to color and opacity parameters is provided for display purposes. After image data is captured and readied for processing, each spatial point in the image is evaluated against predetermined intensity-time parameters. The resulting intensity-time confidence image is then processed to identify distinct regions within the image, and evaluate morphological characteristics of the identified regions using predetermined morphology templates/parameters. A confidence value is determined for each region, and this value is applied to the intensity-time value for each spatial point. The resulting output is a confidence image for the patient's region of interest, that can be used to detect different abnormalities, and display them in a conveniently manipulatable manner so a medical service provider can better understand the abnormality and take more directed actions (e.g., refined procedures) to remedy it as appropriate. The computational system disclosed can thoroughly and automatically detect these temporal patterns, as well as morphological patterns, and not only marks them for visual inspection with a confidence level, but also identifies the type or kind of cancer with an assigned probability, giving accurate indications of the extent of the cancer.