Wyniki wyszukiwania - BazTech

1

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Zarychta P., Badura P., Pietka E.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2017

|

Vol. 65, nr 1

63--70

EN

A study on computer aided diagnosis of posterior cruciate ligaments is presented in this paper. The diagnosis relies on T1-weighted magnetic resonance imaging. During the image analysis stage, the ligament region is automatically detected, localized, and extracted using fuzzy segmentation methods. Eight geometric features are defined for the ligament object. With a clinical reference database containing 107 cases of both healthy and pathological cases, a Fisher linear discriminant is used to select 4 most distinctive features. At the classification stage we employ five different soft computing classifiers to evaluate the feature vector suitability for the computerized ligament diagnosis. Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems. The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the computer aided ligaments diagnosis.

2

Computer aided diagnosis system for abdomen diseases in computed tomography images

Sethi G., Saini B. S.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 1

42--55

EN

In this paper, a computer aided diagnostic (CAD) system for classification of abdomen diseases from computed tomography (CT) images is presented. The methodology used in this paper is to select the most appropriate machine learning technique of segmentation, feature extraction and classification for each module of proposed CAD. The methodology of selecting appropriate machine learning technique for each module of CAD results in accurate and efficient system. Regions of interest are segmented from CT images of tumor, cyst, calculi and normal liver using active contour models, region growing and thresholding. The CAD presented in this research work exploits the discriminating power of features for classifying abdominal diseases. Therefore, feature extraction module extracts statistical texture descriptors using three kinds of feature extraction methods i.e. Gray-Level co-occurrence matrices (GLCM), Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT). At the next stage, effective and optimum features of ROIs are selected using Genetic Algorithm (GA). Further, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to assess the capability of features for classification of diseases of abdomen. The study is performed on 120 CT images of abdomen (30 normal, 30 tumor, 30 cyst and 30 calculi). It is observed from the results that proposed CAD consists of edge based active contour model combined with optimized statistical texture descriptors using DCT along with ANN as classifier achieves the best diagnostic performance of 95.1%. It is also shown in results that proposed CAD achieves highest sensitivity, specificity of 95% and 98% respectively.

3

Zastosowania analizy ruchu oczu w diagnostyce chorób neurodegeneracyjnych

Borys M.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2015

|

nr 2

55--63

PL

Badania okulograficzne są nieinwazyjne, a sprzęt okulograficzny coraz bardziej dostępny, dlatego też rośnie jego popularność w diagnostyce medycznej oraz innych obszarach zastosowań. Artykuł prezentuje możliwości dotyczący zastosowania badań okulograficznych w diagnostyce chorób neurodegeneracyjnych takich jak Parkinson, czy Alzheimer. Autorka koncentruje się na przestawieniu charakterystyki badania okulograficznego, modelu detekcji podstawowych zdarzeń takich jak fiksacje i sakkady, a także na przykładach badań zaburzeń okoruchowych w wybranych chorobach neurodegeneracyjnych.

EN

Eye-tracking allows for non-invasive examination and the same eyetrackers are becoming more available, thus its popularity in medical diagnosis and other fields of application is increasing. The paper presents the possibilities for using eye-tracking in the diagnosis neurodegenerative disease such as Parkinson or Alzheimer diseases. The autor describes the exetracking experiment, the model of fixations and saccedes detection, as well as the selected examples of eye-tracking research conducted among patients with neurodegenerative diseases.