Ashari Yunus

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.

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.

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.

Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (δ) and the probability of Type II Error (β). However, since 1 −  δ is the true positive fraction (TPF) and β is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor.

Texture-Based Statistical Detection and Discrimination of Some Respiratory Diseases Using Chest Radiograph

This chapter proposes a novel texture-based statistical procedure to detect and discriminate lobar pneumonia, pulmonary tuberculosis (PTB), and lung cancer simultaneously using digitized chest radiographs. A modified principal component method applied to wavelet texture measures yielded feature vectors for the statistical discrimination procedure. The procedure initially discriminated between a particular disease and the normals. The maximum column sum energy texture measure yielded 98 % correct classification rates for all three diseases. The diseases were then compared pair-wise, and the combination of mean of energy and maximum value texture measures gave correct classification rates of 70, 97, and 79 % for pneumonia, PTB, and lung cancer, respectively.

Comparing watershed and FCM segmentation in detecting reticular pattern for interstitial lung disease

Lung is an important organ in human respiratory system. However a group of lung diseases known as interstitial lung diseases (ILD) may affect the tissue and space around the air sacs of the lung that prohibit the transferring of enough oxygen into bloodstream. Presently, ILD patients are diagnosed manually by the medical practitioner based on the clinical findings and High-Resolution Computed Tomography (HRCT) thorax images. The process of diagnosing using HRCT images is time-consuming and the outcomes are subjective in nature. One of the indicators of the ILD is the existence of reticular pattern on the HRCT Thorax images. The severity of ILD basically depends on the coarseness of this reticular pattern. The research focuses on the segmentation of the reticular pattern on the infected region based on the grades given by the ILD scoring index; grade 0 - absent, grade 1 - fine intralobular fibrosis predominating, grade 2 - microcystic pattern with airspace less than 3mm in diameter, and grade 3 - larger cysts 3-6mm in diameter. This paper discussed the two segmentation techniques, watershed segmentation algorithm and Fuzzy C-Means (FCM). The study shows that both methods able to segment the reticular pattern for grade 2 and grade 3 of the disease. FCM yielded better result compared to the watershed in term of having higher accuracy of cyst detection and less over-segmented region.

Applying a statistical PTB detection procedure to complement the gold standard.

This paper investigates a novel statistical discrimination procedure to detect PTB when the gold standard requirement is taken into consideration. Archived data were used to establish two groups of patients which are the control and test group. The control group was used to develop the statistical discrimination procedure using four vectors of wavelet coefficients as feature vectors for the detection of pulmonary tuberculosis (PTB), lung cancer (LC), and normal lung (NL). This discrimination procedure was investigated using the test group where the number of sputum positive and sputum negative cases that were correctly classified as PTB cases were noted. The proposed statistical discrimination method is able to detect PTB patients and LC with high true positive fraction. The method is also able to detect PTB patients that are sputum negative and therefore may be used as a complement to the gold standard.

Phase congruency parameter estimation and discrimination ability in detecting lung disease chest radiograph

The conventional chest radiograph remains a widely tool in the diagnosis of lung diseases even to the present day. Current methods or algorithms for disease detection focus on the discrimination between normal images and images with signs of disease involving chest radiograph. This paper proposed a novel algorithm to solve the difficult problem of discriminating two similar diseases, pulmonary tuberculosis (PTB) and lobar pneumonia (PNEU) using phase congruency. The phase congruency PC(x) parameter estimation was studied to obtain the best PC(x)-values that has the ability to differentiate between normals, PTB and PNEU. Eight texture measures of PC(x) values were then investigated as global measures for differentiation of diseases. All eight of these texture measures were found to have univariate normal distributions which allowed the statistical discriminant function, D(x), to select the best texture measures. The homogeneity texture measure gave the best discrimination for PTB and PNEU with Type 1 Error of 0.1 while the Type II Error of 0.15.

Enhanced automatic lung segmentation using graph cut for Interstitial Lung Disease

Radiologists are known to suffer from fatigue and drop in diagnostic accuracy due to large number of slices to read and long working hours. A computer aided diagnosis (CAD) system could help lighten the workload. Segmentation is the first step in a CAD system. This study aims to propose an accurate automatic segmentation. This study deals with High Resolution Computed Tomography (HRCT) scans of the thorax for 15 healthy patients and 81 diseased lungs segregated to five levels based on anatomic landmarks by a senior radiologist. The method used in this study combines thresholding and normalized graph cut which is a combination of region and contour based methods. The way the graph cut is implemented with a rule of exclusion can offer some knowledge for greater accuracy of segmentation. The segmentation was compared to manual tracing done by a trained person who is familiar with lung images. The segmentation yielded 98.32% and 98.07% similarity for right lung (RL) and left lung (LL). The segmentation error of Relative Volume Difference (RVD) for both RL and LL are also low at 0.89% and -0.13% respectively. The Overlap Volume Errors (OVE) are low at 3.17% and 3.74% for RL and LL. Thus the automatic segmentation proposed was able to segment accurately across right and left lung and was able to segment severe diseased lungs.