Microaneurysms Detection using Blob Analysis for Diabetic Retinopathy

Blob analysis is a mathematical method to find the region of interest (ROI) by focusing on the characteristics like brightness or colour. In this work, the process to segment Microaneurysms (MAs) involves two stages, which are pre-processing and segmentation. Pre-processing is a phase for noise removal and illumination correction. In this work, several methods were utilized namely Contrast Limited Adaptive Histogram Equalization (CLAHE), Normalization for contrast enhancement and median filter for noise removal. Then, continue with segmentation phase to segment the MAs from the image. In segmentation phase, several methods were used namely morphological opening, thresholding, Hessian Matrix 2D and Eigenvalue of Hessian Matrix. Finally all the resulting images were compared with the benchmark image to measure the accuracy and grading the stage of Diabetic Retinopathy (DR) by comparing the number of detected MAs. The segmentation accuracy of this project is 68% and 55% accuracy for stage gradin

Illumination and Contrast Correction Strategy using Bilateral Filtering and Binarization Comparison

Illumination normalization and contrast variation on images are one of the most challenging tasks in the image processing field. Normally, the degrade contrast images are caused by pose, occlusion, illumination, and luminosity. In this paper, a new contrast and luminosity correction technique is developed based on bilateral filtering and superimpose techniques. Background pixels was used in order to estimate the normalized background using their local mean and standard deviation. An experiment has been conducted on few badly illuminated images and document images which involve illumination and contrast problem. The results were evaluated based on Signal Noise Ratio (SNR) and Misclassification Error (ME). The performance of the proposed method based on SNR and ME was very encouraging. The results also show that the proposed method is more effective in normalizing the illumination and contrast compared to other illumination techniques such as homomorphic filtering, high pass filter and double mean filtering (DMV)

Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy

Segmentation Based on Morphological Approach for Enhanced Malaria Parasites Detection

Malaria is one of the serious medical issues in the world, with a high frequency of cases in tropical and subtropical regions; further driven by dilapidated living conditions. In 2015, there were approximately 214 million cases of malaria and 438,000 deaths estimated globally, mostly among African children. Malaria develops to become life-threatening without immediate action. Therefore, this paper proposes an image segmentation technique via morphological approach in order to automate the detection of the presence of malaria parasites in malaria image. This technique based on a combination of filtering image and the morphological operator. The effectiveness of the proposed image segmentation approach has been measured by comparing this technique with other segmentation techniques namely, Otsu, Niblack, local adaptive, and Feng methods. Overall, the experimental results indicate that the proposed morphological approach has produced the best segmentation performance with segmentation accuracy and specificity of 98.52% and 99.62%

Biomechanical analysis of patient-specific femur model of osteogenesis imperfecta with cortical and cancellous bone

Osteogenesis imperfecta (OI) is a fragile bone disease characterized by easy fractures. The femur consists of cortical and cancellous bone, each with different mechanical properties. Bone fractures often occur throughout patients’ lifetime. However, doctors still have no quantitative method to predict fractures. This project’s purpose is to investigate the mechanical behaviour of patient-specific OI femur from the finite element analysis. The fracture risk in daily activities (ADL) were examined. All the stress values were judged by the fracture criteria, assumed as 115 MPa. The exercises that exerted force more than 6 times of body weight could cause fractures. Cancellous bone was not affected in any case of ADL. The effects of force and stress on cancellous bone and its impact on fracture risk are negligible

Automated thresholding in radiographic image for welded joints

Automated detection of welding defects in radiographic images becomes non-trivial when uneven illumination, contrast and noise are present. In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu’s thresholding and morphological operations method are utilised to locate the area in which defects might exist. This is followed by the implementation of inverse surface thresholding with partial differential equation to locate isolated areas that represent the defects in the second stage. The proposed method obtained a promising result with high precision

Image Enhancement Technique on Contrast Variation: A Comprehensive Review

Image enhancement is very important, especially for the analysis and diagnosis of detailed information. Most of the studies conducted in image enhancement focus on contrast normalization. Generally, contrast determines how information in images can be perceived easily, how various details in the image can be easy distinguished or how objects of interest can be located. In this paper, a comprehensive review of image enhancement based on spatial domain (Histogram Equalization (HE) and Homomorphic Filtering) and frequency domain (Discrete Wavelet Transform (DWT)) is presented. The improvement and modification of methods were explained systematically. The objective of this work was to study the advantages and drawbacks for each of the method based on a comparison of the results performance. Besides that, this research focuses on various types of applications, emphasizing the importance of contrast enhancement for the improvement of its performance, especially in terms of accuracy and sensitivity. Previous studies were reviewed and critically compared to gain a better understanding of image enhancement. New ideas for further research improvement in image enhancement were proposed

An Improved Sauvola Approach on Document Images Binarization

Document image binarization is one important processing step, especially for data analysis. A variable background, non-uniform illumination, and blur give a big challenging task in order to detect the text. In this paper, a new binarization based on local thresholding technique ‘WAN’ was presented. The proposed algorithm is known as ‘WAN’ after the first name of the author of this paper. WAN has been inspired by the Sauvola’s binarization method and exhibits its robustness and effectiveness when evaluated on low quality document images. Sauvola method failed to segment if the contrast between the foreground and background is small or if the text is in thin pen stroke text. The objective of the WAN method is to improve the Sauvola method and achieved a better binarization result. The results of the numerical simulation indicate that the WAN method is the most effective and efficient (f-measure 72.274 and NRM = 0.093) compared to the Sauvola method, Local Adaptive method, Niblack method, Feng Method, and Bernsen method

Exudates segmentation using inverse surface adaptive thresholding

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms methods based on watershed segmentation and morphological reconstruction. The proposed method obtained 98.2 and 90.4 in terms of sensitivity for Standard Diabetic Retinopathy Database – Calibration Level 1 (DIARETDB1) and a local dataset provided by National University Hospital of Malaysia(NUHM), respectively