Topics in statistical discrimination

Abstract available p. [iv]-[v

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

Malaysian women shoe sizing system using multivariate normal probability distribution

The Malaysian women population frequently face the problem of finding the best fitting shoes. This problem is created by the absence of a Malaysian women shoe sizing system. Standard statistical methods involving the Multivariate Normal distribution are used in a novel process of addressing issues related to the creation of a shoe sizing system, in particular, the problem of defining categories of shoe sizes. This study focused on the use of five-foot measurement namely, foot length (FL), foot breadth (FB), foot's ball girth (BG), instep length (IL), and fibulare instep length (FIL). Univariate hypothesis testing was performed taking advantage of the existence of normal probability distribution. For brevity, details for FL, FB, and BG are shown in this paper, followed by a comparison of performance results between FL, FB, BG and FL, FB, BG, IL, FIL. Our results were compared to a similar study showing almost the same aggregate loss and coverage percentage. The result shows that a modest sample size of 160 was sufficient to define categories of shoe sizes to help develop a prototype shoe sizing system using the proposed novel approach. The proposed prototype shoe sizing system provides information for the planning, design, and manufacturing of Malaysian women's footwear with implications for better fitness and comfort

Lung disease classification using GLCM and deep features from different deep learning architectures with principal component analysis

Lung disease classification is an important stage in implementing a Computer Aided Diagnosis (CADx) system. CADx systems can aid doctors as a second rater to increase diagnostic accuracy for medical applications. It has also potential to reduce waiting time and increasing patient throughput when hospitals high workload. Conventional lung classification systems utilize textural features. However textural features may not be enough to describe properties of an image. Deep features are an emerging source of features that can combat the weaknesses of textural features. The goal of this study is to propose a lung disease classification framework using deep features from five different deep networks and comparing its results with the conventional Gray-level Co-occurrence Matrix (GLCM). This study used a dataset of 81 diseased and 15 normal patients with five levels of High Resolution Computed Tomography (HRCT) slices. A comparison of five different deep learning networks namely, Alexnet, VGG16, VGG19, Res50 and Res101, with textural features from Gray-level Co-occurrence Matrix (GLCM) was performed. This study used a K-fold validation protocol with K = 2, 3, 5 and 10. This study also compared using five classifiers; Decision Tree, Support Vector Machine, Linear Discriminant Analysis, Regression and k-nearest neighbor (k-NN) classifiers. The usage of PCA increased the classification accuracy from 92.01% to 97.40% when using k-NN classifier. This was achieved with only using 14 features instead of the initial 1000 features. Using SVM classifier, a maximum accuracy of 100% was achieved when using all five of the deep learning features. Thus deep features show a promising application for classifying diseased and normal lungs

Comparison of super resolution methods in magnetic resonance images for small animals

Super resolution (SR) is an array of methods that utilize different approaches to enhance the quality and resolution of an image. This is particularly useful for images that are very small and have low quality. Small images are usually obtained due to the limitation of imaging capture systems or the subject captured is small. For small animals such as rats, imaging can be difficult and expensive to produce high-resolution images. Therefore, SR is a very relevant field of study for small animals. The study of small animals involve many impactful fields such as testing of harmful chemicals on the biological processes. The objective of this study is to compare 11 SR methods for rat magnetic resonance images (MRI). This study is a pilot study and the beginning of the research to see the effect of kratom that is a hallucinogen misused in south east Asia. This study used chosen images from six rats. These MRI images were captured at UM MRI Research Centre. This study compared the quality of SR methods using several measures including Peak Sound to Noise Ratio (PSNR), Sound to Noise Ratio (SNR), Mean Square Error (MSE), Structural Similarity Index (SSI). This study compared these methods on two different size factors of resolution which were two and four. The results show promising results for the next stage of research

A vision problem in wire bonding

A requirement for good wire bonding is the existence of intermetallics between the gold ball and bond pad. A digital image of the surface of the gold ball that was attached to the bond pad was obtained and analysed with MATLAB. The `counting method' (CM) is proposed for determining the percentage of intermetallics formation. Four experiments were performed to investigate the reliability of CM, the effect of light-offset, and the performance of a quality assistant (QA) in his ability to visually estimate (the visual method) the intermetallics coverage. The main results indicate that the visual method is inconsistent and less accurate than the counting method. The QA performs well for high intermetallics coverage, whilst for an approximately 50% intermetallics coverage the QA tends to overestimate. Critical remarks related to modeling the digital image are presente

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 (α=.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 statistical method for comparing chest radiograph images in MTB patients

Digital images of chest radiograph taken at different time points may be compared to investigate the effect of treatment on mycobacterium tuberculosis (MTB) patients. One method of comparison is that of visually locating “snow-flakes� which should decrease in area or size with each subsequent image. This paper propose a more objective method; the comparison of image histograms whereby a leftward shift of the histogram indicates a positive effect of treatment. The comparison of two histograms is equivalent to either comparing the corresponding box-plots or the corresponding set of percentiles. However, before the comparison is made the images need to be registered and resized. The results of this study show that the proportion of percentiles (from histogram) can be used as an indicator of treatment effect (or patient’s progress). Further the correlations, RF2, is shown to be the best similarity measure to indicate the quality of image registration. Finally, this study also shows that a combination of registration and resizing can improve the pair-wise comparison

Critical remarks on some applications of digital image analysis with emphasis on statistical methodology

The results of Rijal and Noor [1] and [2] regarding the use of statistical methods and sources of uncertainty associated with making inferences when using digital images provide the motivation for this study. In this paper three other examples are presented with the purpose of providing an overview of applications (possibly statistical) of image analysis, and general issues highlighted. One conclusion from this study is that statistical/mathematical methodology should be emphasized in digital image analysis

Statistical clustering of maxillary dental arches

The purpose of this study was to define a procedure for grouping Malaysian dental arches into clusters by applying the agglomerative hierarchical clustering (AHC) method. Standardized digital images of maxillary dental casts of 170 subjects were used to measure the distance joining left and right hamular notches, a, and the perpendicular distance between this line and the incisive papilla, b. Coefficients of the fitted quadratic curve (α2, α1 and α0) were calculated using selected landmarks on the casts. The variables a, b, α2, α1 and α0 were then used to represent the shape of each dental cast. Subsequently, casts were randomly divided into 2 subsamples; control and test samples. The AHC method was applied to the control sample to establish clusters. To verify the clusters formed, 40 test samples were assigned to the clusters. The number of acceptable clusters was established when no cluster had less than 4 members (10% of the test samples). The total number of members in all formed clusters was at least 36 (90% of the test samples) and the margin of error, h was 5 mm (least acceptable value). Using the AHC method, maxillary dental arches may be grouped into 3 clusters as defined by the median values of the proposed shape parameters investigated; (46.88 mm, 47.83 mm, 5.12, 0.55,-57.20), (47.31 mm, 43.21 mm, 4.89, 0.11, -53.52) and (51.51 mm, 50.09 mm, 4.85, 0.05, -60.74) respectively