An FPGA Based Coprocessor for GLCM and Haralick Texture Features and Their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

Interobserver agreement of the Gross Motor Function Classification System in an ambulant population of children with cerebral palsy

Gross Motor Function Classification System (GMFCS) level was reported by three independent assessors in a population of children with cerebral palsy (CP) aged between 4 and 18 years (n=184; 112 males, 72 females; mean age 10y 10mo [SD 3y 7mo]). A software algorithm also provided a computed GMFCS level from a regional CP registry. Participants had clinical diagnoses of unilateral (n=94) and bilateral (n=84) spastic CP, ataxia (n=4), dyskinesia (n=1), and hypotonia (n=1), and could walk independently with or without the use of an aid (GMFCS Levels I-IV). Research physiotherapist (n=184) and parent/guardian data (n=178) were collected in a research environment. Data from the child's community physiotherapist (n=143) were obtained by postal questionnaire. Results, using the kappa statistic with linear weighting (?1w), showed good agreement between the parent/guardian and research physiotherapist (?1w=0.75) with more moderate levels of agreement between the clinical physiotherapist and researcher (?1w=0.64) and the clinical physiotherapist and parent/guardian (?1w=0.57). Agreement was consistently better for older children (>2y). This study has shown that agreement with parent report increases with therapists'experience of the GMFCS and knowledge of the child at the time of grading. Substantial agreement between a computed GMFCS and an experienced therapist (?1w=0.74) also demonstrates the potential for extrapolation of GMFCS rating from an existing CP registry, providing the latter has sufficient data on locomotor ability.

Calibration of assessment instruments – classification in mathematics at the Universidad Jorge Tadeo Lozano

Item Response Theory, IRT, is a valuable methodology for analyzing the quality of the instruments utilized in assessment of academic achievement. This article presents an implementation of the mentioned theory, particularly of the Rasch model, in order to calibrate items and the instrument used in the classification test for the Basic Mathematics subject at Universidad Jorge Tadeo Lozano. 509 responses chains of students, obtained in the june 2011 application, were analyzed with a set of 45 items, through eight case studies that are showing progressive steps of calibration. Criteria of validity of items and of whole instrument were defined and utilized, to select groups of responses chains and items that were finally used in the determination of parameters which then allowed the classification of assessed students by the test.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

The classification and diagnosis of erythrocytosis

An absolute erythrocytosis is present when the red cell mass is raised and the haematocrit is elevated above prescribed limits. Causes of an absolute erythrocytosis can be primary where there is an intrinsic problem in the bone marrow and secondary where there an event outside the bone marrow driving erythropoiesis. This can further be divided into congenital and acquired causes. There remain an unexplained group idiopathic erythrocytosis. Investigation commencing with thorough history taking and examination and then investigation depending on initial features is required. Clear simple criteria for polycythaemia vera are now defined. Those who do not fulfil these criteria require further investigation depending on the clinical scenario and initial results. The erythropoietin level provides some guidance as to the direction in which to proceed and the order and extent of investigation necessary in an individual patient. It should thus be possible to make an accurate diagnosis in the majority of patients.