Omar Mohd. Rijal

Regression Methods to Investigate the Relationship Between Facial Measurements and Widths of the Maxillary Anterior Teeth

STATEMENT OF PROBLEM In rehabilitating edentulous patients, selecting appropriately sized teeth in the absence of preextraction records is problematic. PURPOSE The purpose of this study was to investigate the relationships between some facial dimensions and widths of the maxillary anterior teeth to potentially provide a guide for tooth selection. MATERIAL AND METHODS Sixty full dentate Malaysian adults (18-36 years) representing 2 ethnic groups (Malay and Chinese), with well aligned maxillary anterior teeth and minimal attrition, participated in this study. Standardized digital images of the face, viewed frontally, were recorded. Using image analyzing software, the images were used to determine the interpupillary distance (IPD), inner canthal distance (ICD), and interalar width (IA). Widths of the 6 maxillary anterior teeth were measured directly from casts of the subjects using digital calipers. Regression analyses were conducted to measure the strength of the associations between the variables (alpha=.10). RESULTS The means (standard deviations) of IPD, IA, and ICD of the subjects were 62.28 (2.47), 39.36 (3.12), and 34.36 (2.15) mm, respectively. The mesiodistal diameters of the maxillary central incisors, lateral incisors, and canines were 8.54 (0.50), 7.09 (0.48), and 7.94 (0.40) mm, respectively. The width of the central incisors was highly correlated to the IPD (r=0.99), while the widths of the lateral incisors and canines were highly correlated to a combination of IPD and IA (r=0.99 and 0.94, respectively). CONCLUSIONS Using regression methods, the widths of the anterior teeth within the population tested may be predicted by a combination of the facial dimensions studied.

A discrimination method for the detection of pneumonia using chest radiograph.

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.

Segmentation of the Lung Anatomy for High Resolution Computed Tomography (HRCT) Thorax Images

In diagnosing interstitial lung disease (ILD) using HRCT Thorax images, the radiologists required to view large volume of images (30 slices scanned at 10 mm interval or 300 slices scanned at 1 mm interval). However, in the development of scoring index to assess the severity of the disease, viewing 3 to 5 slices at the predetermined levels of the lung is suffice for the radiologist. To develop an algorithm to determine the severity of the ILD, it is important for the computer aided system to capture the main anatomy of the chest, namely the lung and heart at these 5 predetermined levels. In this paper, an automatic segmentation algorithm is proposed to obtain the shape of the heart and lung. In determine the quality of the segmentation, ground truth or manual tracing of the lung and heart boundary done by senior radiologist was compared with the result from the proposed automatic segmentation. This paper discussed five segmentation quality measurements that are used to measure the performance of the proposed segmentation algorithm, namely, the volume overlap error rate (VOE), relative volumetric agreement (RVA), average symmetric surface distance (ASSD), root mean square surface distance (RMSD) and Hausdorff distance (HD). The results showed that the proposed segmentation algorithm produced good quality segmentation for both right and left lung and may be used in the development of computer aided system application.

Determining features for discriminating PTB and normal lungs using phase congruency model

The appearance of the infected zone on the digital chest X-ray image for pulmonary tuberculosis (PTB) does not conform to standard shape, size or configuration. This study uses phase congruency (PC(x)) values to gather information from transition of adjacent pixel values that may be used as features to represent known disease type. The feature vector consisting of the average, variance, coefficient of variation and maximum PC(x)-values was found to be able to detect PTB with high accuracy.

A statistical interpretation of the chest radiograph for the detection of pulmonary tuberculosis

This paper presents a statistical interpretation of the chest radiograph for the detection of pulmonary tuberculosis (PTB). Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q. The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The most important result of this study recommends the detection of pulmonary tuberculosis by constructing discriminant function using maximum column sum energy texture measures where the misclassification probabilities were less than 0.15. In the validation exercise, the proposed discriminant procedure yielded 94% correct classification rate.

Wavelet as features for tuberculosis (MTB) using standard X-ray film images

The success of eliminating the disease Mycobacterium Tuberculosis (MTB) depends on the detection capabilities of medical organizations. In Malaysia government hospitals perform the major part of this particular task. An important ingredient of the diagnostic process in government hospital is the visual interpretation of standard chest X-ray films. Problems associated with this type of visual interpretation are well known. In this study, we propose an objective method, involving wavelets, for the purpose of detecting MTB.

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model This paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). The X-graph and Y-graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using the X-graph and the Y-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R2p) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.

Double segmentation method for brain region using FCM and graph cut for CT scan images

In the field of neuropsychiatric disorders, it is known that brain segmentation is important for both detection and diagnosis. The segmentation of the brain, which leads to the computation of brain volume proved to be vital in the detection of many brain pathology having Computed Tomography (CT) scan as the primary modality. Due to the fact that Fuzzy c-Means (FCM) proven to be robust, it is often used in data clustering and also in image segmentation. On the other hand, Graph cut is also a great segmentation algorithm for image segmentation as it allows the separation of the image into numerous partitions according to the similarity between each nodes in the image. In this paper, FCM was first used as global processing on CT scan images that separated the images into clusters based on pixel intensity. After that, local processing with graph cut algorithm was carried out on the automatically selected cluster from the FCM. Manual interaction is needed after the images were separated into partitions to select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.

Texture-based classification for reticular pattern and ground glass opacity in high resolution computed tomography Thorax images

Lung disease is a global disease that affects a large group of people and is of growing interest to researches. The role of Computer Aided Diagnosis (CAD) systems to assist doctors to diagnose and detect disease is a beneficial one. Most techniques used to classify diseases stem from the textural method that is commonly associated with Gray Level Co-occurrence Matrix (GLCM). For this study, the objective is to classify the presence of two medical features in lung diseases which are Reticular Pattern (RP) and Ground Glass Opacity (GGO). A senior radiologist rates each slice and lung of a patient for the RP and GGO. Five slices of predetermined level of HRCT Thorax images that representing the whole lung of ten diseased patients and ten normal patients were used in this study. The textural features are extracted from each patient using the GLCM method. Classification was done using the WEKA, a machine learning tool. The classifiers used were the Naive Bayes, Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Radial Basis Function Network, Random Forest and Decision Table classifiers. The classifiers showed that it is possible to have a classifier with an overall accuracy of 0.81 with RF classifier.

Performance Evaluation of Lung Segmentation

Segmentation of structures in medical images is challenging due to several factors which include anatomical differences, abnormalities in lung tissue, image noise, and differences in acquisition parameters. Segmentation is one of the key initial components of Computer Aided Diagnosis (CAD) system. Radiologists deal with a heavy workload of having to examine a large number of high resolution computed tomography (HRCT) images. CAD-based systems can lighten their load. and also aiding them as a tool in their diagnostic evaluations. The development of CAD and hence the automatic segmentation is aggressively pursued by researchers worldwide. Therefore it is important to determine the performance or the quality of the automated segmentation developed. Here in this chapter, different segmentation performance evaluation methods for medical images are presented. For most segmentation evaluations, it is important to have comparison done with the gold standard. The gold standard for segmentation involving medical images is the delineation or manual tracing of the region of interest done by a human expert who is preferably a radiologist. The smaller the deviation of the segmentation compared to the human expert, the higher the performance or the quality of the segmentation. The performance evaluation for the segmentation is divided into quantitative and qualitative methods. Most quantitative methods fall into two categories which are area based evaluation methods where the difference between the areas of segmentation and the gold standard are compared, and surface evaluation type where the method evaluates based on the difference between contours of the segmentation and the gold standard. This chapter also discussed a performance evaluation of an automated lung segmentation systems (ALSS) developed by UTM Razak School. This chapter shows the vast performance measures available for determining the segmentation quality. More than one type of performance measure should be used to give a broader and unbiased view of the segmentation quality.