Markov random field-based clustering applied to the segmentation of masses in digital mammograms.

In this paper we propose a new pixel clustering model applied to the analysis of digital mammograms. The clustering represents here the first step in a more general method and aims at the creation of a concise data-set (clusters) for automatic detection and classification of masses, which are typically among the first symptoms analysed in early diagnosis of breast cancer. For the purpose of this work, a set of mammographic images has been employed, that are 12-bit gray level digital scans and as such, are inherently inhomogeneous and affected by the noise resulting from the film scanning. The image pixels are described only by their intensity (gray level), therefore, the available information is limited to one dimension. We propose a Markov random field (MRF)-based technique that is suitable for performing clustering in an environment which is described by poor or limited data. The proposed method is a statistical classification model, that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics, that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the output. Moreover, we define a probabilistic description of the model, that is characterised by a remarkable simplicity, such that its realisation can be easily and efficiently implemented in any high- or low-level programming language, thus allowing it to be run on virtually any kind of platform. Finally, we evaluate the algorithm against the classical K-means clustering routine. We point out similarities between the two methods and, moreover, show the advantages and superiority of the MRF scheme.

CT analysis of defects of the cement mantle and alignment of the stem: in vitro comparison of Charnley-Kerboul femoral hip implants inserted line-to-line and undersized in paired femora.

Using a modern cementing technique, we implanted 22 stereolithographic polymeric replicas of the Charnley-Kerboul stem in 11 pairs of human cadaver femora. On one side, the replicas were cemented line-to-line with the largest broach. On the other, one-size undersized replicas were used (radial difference, 0.89 mm sd 0.13).CT analysis showed that the line-to-line stems without distal centralisers were at least as well aligned and centered as undersized stems with a centraliser, but were surrounded by less cement and presented more areas of thin (< 2 mm) or deficient (< 1 mm) cement. These areas were located predominantly at the corners and in the middle and distal thirds of the stem. Nevertheless, in line-to-line stems, penetration of cement into cancellous bone resulted in a mean thickness of cement of 3.1 mm (sd 0.6) and only 6.2% of deficient and 26.4% of thin cement. In over 90% of these areas, the cement was directly supported by cortical bone or cortical bone with less than 1 mm of cancellous bone interposed. When Charnley-Kerboul stems are cemented line-to-line, good clinical results are observed because cement-deficient areas are limited and are frequently supported by cortical bone.

Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors.

Describes a knowledge-based image interpretation system for the segmentation and labeling of a series of 2-D brain X-ray CT-scans, parallel to the orbito-meatal plane. The system combines the image primitive information produced by different low level vision techniques in order to improve the reliability of the segmentation and the image interpretation. It is implemented in a blackboard environment that is holding various types of prior information and which controls the interpretation process. The scoring model is applied for the fusion of information derived from three types of image primitives (points, edges, and regions). A model, containing both analogical and propositional knowledge on the brain objects, is used to direct the interpretation process. The linguistic variables, introduced to describe the propositional features of the brain model, are defined by fuzzy membership functions. Constraint functions are applied to evaluate the plausibility of the mapping between image primitives and brain model data objects. Procedural knowledge has been integrated into different knowledge sources. Experimental results illustrate the reliability and robustness of the system against small variations in slice orientation and interpatient variability in the images.