Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.

Background: Semiquantitative evaluation and manual cell counting are the commonly used procedures to assess positive staining of molecular markers in tissue sections. Manual counting is also a laborious task in which consistent objectivity is difficult to achieve. Recently, image analysis has been explored, but the studies reported were limited to histological images acquired at high magnification and containing uniformly stained cells.Methods: The analyzed material consisted of histological sections from different squamous cell cancers that had stained for proliferation using Ki‐67 and cyclin A detection. The first step of the method was based on detecting the overall number of cells irrespective to their stain, using second‐order edge detection methodology. Then proliferating cells were located using principal component analysis (PCA) of the color image, combined with histogram thresholding.Results: The algorithms' performances were validated on tissue section images encountered in routine clinical practice by comparison with objective measures of performance and manual cell identification. The algorithms correlated closely with manual counting of all cells (r² = 0.96–0.97) and stained cells (4–7% cell count error).Conclusions: Cell counting in complex large‐scale histological images could be applied in routine practice using edge and color information. The proposed technique provides several benefits, such as speed of analysis, consistency, and automation. Moreover, it is faster than human observation and could replace the laborious task of manual cell counting. Cytometry Part A 55A:30–42, 2003.

Background: The mechanisms behind motor recovery from stroke are not clearly understood. Functional imaging studies have demonstrated task-related brain activation in several motor areas, but few studies have attempted to correlate this with stroke outcome. Moreover, these studies have focused on how motor areas may individually contribute to compensation. Here, the authors investigate whether different cortical areas interact to form dynamic assemblies that may then compensate for disability. Methods: The authors investigated corticocortical coherence in 16 healthy subjects and 25 patients with chronic stroke involving one cerebral hemisphere and having varying degrees of motor recovery. Scalp EEG was recorded at rest and while right-handed subjects performed a unimanual grip task. The degree of functional recovery after stroke was assessed using a range of outcome measures. Results: Compared with healthy subjects, hand-related asymmetries in task-related EEG-EEG coherence were increased between mesial and lateral frontal regions of the affected hemisphere, over mesial frontal regions, and over lateral frontal areas of the unaffected hemisphere when patients with stroke gripped with their affected hand. Mesial hand-related asymmetries in task-related power and coherence were negatively correlated with recovery. Conclusion: Increases in task-related coupling between cortical areas may dynamically compensate for brain damage after stroke. Some of this increased coupling, particularly that over mesial frontal areas, decreases as patients make a functional recovery.