Classification of schizophrenia using feature-based morphometry.

The objective of this study was to use a combined local descriptor, namely scale invariance feature transform (SIFT), and a non linear support vector machine (SVM) technique to automatically classify patients with schizophrenia. The dorsolateral prefrontal cortex (DLPFC), considered a reliable neuroanatomical marker of the disease, was chosen as region of interest (ROI). Fifty-four schizophrenia patients and 54 age- and gender-matched normal controls were studied with a 1.5T MRI (slice thickness 1.25 mm). Three steps were conducted: (1) landmark detection and description of the DLPFC, (2) feature vocabulary construction and Bag-of-Words (BoW) computation for brain representation, (3) SVM classification which adopted the local kernel to implicitly implement the feature matching. Moreover, a new weighting approach was proposed to take into account the discriminant relevance of the detected groups of features. Substantial results were obtained for the classification of the whole dataset (left side 75%, right side 66.38%). The performances were higher when females (left side 84.09%, right side 77.27%) and seniors (left side 81.25%, right side 70.83%) were considered separately. In general, the supervised weighed functions increased the efficacy in all the analyses. No effects of age, gender, antipsychotic treatment and chronicity were shown on DLPFC volumes. This integrated innovative ROI-SVM approach allows to reliably detect subjects with schizophrenia, based on a structural brain marker for the disease such as the DLPFC. Such classification should be performed in first-episode patients in future studies, by considering males and females separately.

Brain morphometry by probabilistic latent semantic analysis.

The paper propses a new shape morphometry approach that combines advanced classification techniques with geometric features to identify morphological abnormalities on the brain surface. Our aim is to improve the classification accuracy in distinguishing between normal subjects and schizophrenic patients. The approach is inspired by natural language processing. Local brain surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To do this, a generative model, the probabilistic. Latent Semantic Analysis is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input of a Support Vector Machine (SVM), defining an hybrid generative/discriminative classification algorithm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13%.

DCE-MRI data analysis for cancer area classification.

OBJECTIVES: The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investigating the development of tumor microvessels. The main contribution consists in proposing a machine learning methodology to segment automatically these MRI data, by isolating tumor areas with different meaning, in a histological sense. METHODS: The proposed approach is based on a three-step procedure: i) robust feature extraction from raw time-intensity curves, ii) voxel segmentation, and iii) voxel classification based on a learning-by-example approach. In the first step, few robust features that compactly represent the response of the tissue to the DCE-MRI analysis are computed. The second step provides a segmentation based on the mean shift (MS) paradigm, which has recently shown to be robust and useful for different and heterogeneous clustering tasks. Finally, in the third step, a support vector machine (SVM) is trained to classify voxels according to the labels obtained by the clustering phase (i.e., each class corresponds to a cluster). Indeed, the SVM is able to classify new unseen subjects with the same kind of tumor. RESULTS: Experiments on different subjects affected by the same kind of tumor evidence that the extracted regions by both the MS clustering and the SVM classifier exhibit a precise medical meaning, as carefully validated by the medical researchers. Moreover, our approach is more stable and robust than methods based on quantification of DCE-MRI data by means of pharmacokinetic models. CONCLUSIONS: The proposed method allows to analyze the DCE-MRI data more precisely and faster than previous automated or manual approaches.

A confidence-based approach to enhancing underwater acoustic image formation.

This paper describes a flexible technique to enhance the formation of short-range acoustic images so as to improve image quality and facilitate the tasks of subsequent postprocessing methods. The proposed methodology operates as an ideal interface between the signals formed by a focused beamforming technique (i.e., the beam signals) and the related image, whether a two-dimensional (2-D) or three-dimensional (3-D) one. To this end, a reliability measure has been introduced, called confidence, which allows one to perform a rapid examination of the beam signals and is aimed at accurately detecting echoes backscattered from a scene. The confidence-based approach exploits the physics of the process of image formation and generic a priori knowledge of a scene to synthesize model-based signals to be compared with actual backscattered echoes, giving, at the same time, a measure of the reliability of their similarity. The objectives that can be attained by this method can be summarized in a reduction in artifacts due to the lowering of the side-lobe level, a better lateral resolution, a greater accuracy in range determination, a direct estimation of the reliability of the information acquired, thus leading to a higher image quality and hence a better scene understanding. Tests on both simulated and actual data (concerning both 2-D and 3-D images) show the higher efficiency of the proposed confidence-based approach, as compared with more traditional techniques.

Structured neural networks for pattern recognition.

This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.