Ahmed Mueen

Automatic Multilevel Medical Image Annotation and Retrieval

Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.

An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection

This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Automatic classification of medical X-ray images using a bag of visual words

A novel approach is presented to gain high classification rate for each class of ImageCLEF 2007 medical database. The learning phase consists of four iterations where different classification models were generated as per iteration. For the iterations, a model generation process was performed in two steps. The first step starts with construction of a model from the entire dataset. This model was then assessed to filter high accuracy classes (HAC). These classes were those predicted with an accuracy rate above 80%. This evaluation performed on 20% of the training dataset was taken as test data. In the second step, classes under HAC were only used to construct the classification model. The same processes will be performed in the next iteration on the classes which were left with accuracy below 80% from the previous iteration. The methodology presented is based on a bag of visual words for feature extraction and the radial basis function (RBF)-based support vector machine classifier. As a result, four classification models were generated from 77, 17, 12 and 10 classes, respectively. These models were constructed and evaluated on a database consisting of 11 000 medical X-ray images (training dataset) and 1000 (testing dataset) of 116 classes. The accuracy rate obtained by each generated model outperformed the results obtained by only one model on the entire dataset.

An efficient method for vehicle license plate xtraction

License Plate Recognition (LPR) is a machine vision technology used to identify vehicles by their license plates without direct human intervention. This paper presents an efficient license plate extraction method which is mainly designed for Malaysian license plates recognition. Besides, it can be readily extended to cope with license plates of other countries, especially those using Latin characters. Our method is capable of extracting license plates in a concise manner, it works well on different types of Malaysian license plates and fast enough to be used in real-time. The method has been tested over a large number of images in order to analyze its performance. The test results, demonstrate that the proposed method is efficient to be used for the license plate recognition system.

MIARS: A Medical Image Retrieval System

The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.

Combined Feature Extraction on Medical X-ray Images

Medical images form an essential source of information for various important processes such as diagnosis of diseases, surgical planning, medical reference, research and training. Therefore, effective and meaningful search and classification of these images are vital. In this paper, the approaches of content-based image retrieval (CBIR) using low level features such as shape and texture are investigated in order to create a framework that classify medical X-ray image automatically. Gray level Co-occurrence Matrix, Canny Edge Operator, Local Binary Pattern and pixel level information of the images in this work act as image based feature representations which are adopted in our method. The state-of-the-art machine learning method, Support Vector Machine (SVM) is used for classification. In addition, the performance of image classification offered by combining the promising features stated above is investigated. Experimental results using 116 different classes of 11,000 X-ray images showed 90.7% classification accuracy.

Ontology-Based Clinical Decision Support System for Predicting High-Risk Pregnant Woman

According to Pakistan Medical and Dental Council (PMDC), Pakistan is facing a shortage of approximately 182,000 medical doctors. Due to the shortage of doctors; a large number of lives are in danger especially pregnant woman. A large number of pregnant women die every year due to pregnancy complications, and usually the reason behind their death is that the complications are not timely handled. In this paper, we proposed ontology-based clinical decision support system that diagnoses high-risk pregnant women and refer them to the qualified medical doctors for timely treatment. The Ontology of the proposed system is built automatically and enhanced afterward using doctor’s feedback. The proposed framework has been tested on a large number of test cases; experimental results are satisfactory and support the implementation of the solution.