Segmentations are often necessary for the analysis of image data. They are used to identify different objects, for example cell nuclei, mitochondria, or complete cells in microscopic images. There might be features in the data, that cannot be detected by segmentation approaches directly, because they are not characterized by their texture of boundaries, which are properties most segmentation te...

An image segmentation scheme is shown to be exceptionally successful through the application of high-level knowledge of the required image objects (cell nuclei). By tuning the algorithm’s single parameter, it is shown that the performance can be maximised for the dataset, but leads to individual failures that may require alternative choices. A second stage is introduced to process each of the r...

We present graphical models that enhance a speech recognizer with information about syllabic segmentations. The segmentations are specified by locations of syllable nuclei, and the graphical models are able to use these locations to specify a “soft” segmentation of the speech data. The graphs give improved discrimination between speech and noise when compared to a baseline model. When using loc...

This paper describes three cytological image segmentation methods. The analysis includes the watershed algorithm, active contouring and a cellular automata GrowCut method. One can also find here a description of image pre-processing, Hough transform based pre-segmentation and an automatic nuclei localization mechanism used in our approach. Preliminary experimental results collected on a benchma...

This paper presents the idea of a batch form of the Hough transform, the Compact Hough Transform (CHT), which aims at detecting locations with high probability of belonging to a shape of an approximate size. The CHT is used in a problem of segmentation of cell nuclei, where the nuclear boundary is detected using a method based on maximum likelihood.

Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis ...

The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segment...

Segmentation of intact cell nuclei in three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a...

Meningioma brain tumour discrimination is challenging as many histological patterns are mixed between the different subtypes. In clinical practice, dominant patterns are investigated for signs of specific meningioma pathology; however the simple observation could result in interand intra-observer variation due to the complexity of the histopathological patterns. Also employing a computerised fe...

This paper describes an image analysis technique for the counting of nuclei in mitosis in tissue sections. Five experienced pathologists scored mitoses in photographs of preselected areas of tissue sections of the breast. Objects consistently labelled as mitotic cells by all five pathologists were considered "mitoses" in the analysis. In total, there were 45 mitotic nuclei, 68 possible mitotic ...