Haniza Yazid - Profile on Academia.edu (original) (raw)

Papers by Haniza Yazid

Analysis on Weighted Average Between Features in Dictionary Learning and Sparse Representation Algorithms for Low-Resolution Images

Springer eBooks, 2021

Performance analysis on dictionary learning and sparse representation algorithms

Multimedia Tools and Applications, Mar 3, 2022

Discontinuities Classification Using Texture Features and Support Vector Machine

Springer eBooks, 2022

Analysis on Clustering Based Method for Diabetic Retinopathy Using Color Information

Springer eBooks, 2022

Effect of Image Thresholding on the Homogenized Properties of Trabecular Bone Model

Springer eBooks, 2022

Comparison Between K-Nearest Neighbor (KNN) and Decision Tree (DT) Classifier for Glandular Components

Springer eBooks, 2022

Holonomic Mobile Robot Planners: Performance Analysis

Springer eBooks, 2022

Analysis on Single-Image Super-Resolution (SISR) Using Dictionary Learning and Sparse Representation Algorithm

Springer eBooks, 2022

A Review on Edge Detection on Osteogenesis Imperfecta (OI) Image using Fuzzy Logic

Journal of physics, Oct 1, 2021

Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fractu... more Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fracture. The patient suffered from this disease will have poor quality of life. Simulation on the bone fracture risk would help medical doctors to make decision in their diagnosis. Detection of edges from the OI images is very important as it helps radiologist to segmentize cortical and cancellous bone to make a good 3D bone model for analysis. The purpose of this paper is to review the fundamentals of fuzzy logic in edge detection of OI bone as it is yet to be implemented. Several fuzzy logic concepts are reviewed by previous studies which include fuzziness, membership functions and fuzzy sets regarding digital images. The OI images were produced by modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, or Computed Tomography (CT). In summary, researchers from the reviewed papers concluded that fuzzy logic can be implemented to detect edges in noisy clinical images.

Performance analysis of Otsu thresholding for sign language segmentation

Multimedia Tools and Applications, Mar 16, 2021

Sign language recognition system generally consists of three main processes, which are segmentati... more Sign language recognition system generally consists of three main processes, which are segmentation, modelling, and classification. Image segmentation plays a crucial role as the initial step in sign language recognition. Despite the many sign language recognition system algorithms proposed in the literature and their well-understood usage, their performance analyses are relatively limited. As such, the main motivation of this paper is to critically analyse the feasibility of successful sign language segmentation under variation of dynamic scene parameters such as noise, hand size, and intensity difference between hand and background. The focus is on image thresholding using Otsu technique, since it is the most commonly used in initial process of sign language segmentation. The analysis of this work was developed based on Monte Carlo statistical method, which showed that the success of sign language segmentation depends on hand size, hand background intensity difference, and noise measurement. The result showed that the sign alphabets with handheld shape like A, E, I, M, N, S, and T is easier to segment, while sign alphabets with finger-extend shape like C, D, F, G, H, K, L, P, R, U, V, W, and Y is harder to segment. Experiment using real images demonstrate the capability of the conditions to correctly predict the outcome of sign language segmentation using Otsu technique. In conclusion, the success of sign language segmentation could be predicted beforehand with obtainable scene parameters.

Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding

Measurement, Jul 1, 2023

Performance Analysis of Adaptive Unsharp Masking Filter Techniques for Image Contrast Enhancement

Image contrast enhancement is known as one of the important techniques applied in the field of im... more Image contrast enhancement is known as one of the important techniques applied in the field of image processing. In order to improve the contrast of the captured image, different adaptive Unsharp Masking Filter (UMF) techniques were proposed by the researchers. In this paper, the main contribution is the implementation of three algorithms namely adaptive gain adjustment approach using an UMF (ASAUMF), design of UMF kernel and gain using Particle Swarm Optimization (UMFKG) and lastly, intensity and edge-based adaptive UMF (IntEdgUMF) which is denoted as Algorithm 1, 2 and 3 respectively. These algorithms were tested on the standard and biometric images like face images. This is because these adaptive UMF were mainly applied to natural scenery, but the importance of high image quality is not limited to the environment but also to the other fields such as biometric identification. Based on the results, Algorithm 1 is able to achieve the highest average PSNR values of 31.6079 dB and 35.8052 dB when applied on Set14 and LFW databases respectively. Although Algorithm 1 needs a longer running time in producing the output images, this algorithm can emphasize the details or information from the input image by enhancing the contrast of the image. Thus, Algorithm 1 can be concluded as the best adaptive UMF techniques among the three algorithms tested. For future work, the use of these adaptive UMF can be implemented onto various images, for instance gray scale images or other biometric images in order to test the effectiveness of the algorithms in different applications.

International Journal of Integrated Engineering, Sep 15, 2019

Blob analysis is a mathematical method to find the region of interest (ROI) by focusing on the ch... more 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 grading.

Journal of physics, Nov 1, 2019

Osteogenesis Imperfecta (OI) is a genetic bone disorders that mainly affect the bones which commo... more Osteogenesis Imperfecta (OI) is a genetic bone disorders that mainly affect the bones which commonly leads to the multiple fractures. The purpose of this study is to segment the cortical and cancellous bone of tibia affected with the OI from the CT images. This project consists of two sections, segmentation of the cortical and cancellous bone, and the evaluation of the image performance. Contrast adjustment was implemented to enhance the contrast of images. K-Means and multi-threshold were implemented to segment the cortical bone at the proximal, diaphysis, and distal tibia. Post-processing was applied to further refine the segmented images. All segmented images were then evaluated by using the ground truth images. From the results, the process with contrast adjustment and multi-threshold obtained the highest accuracy and Dicecoefficient of 99.91% and 81.42% respectively at the proximal region while 99.93% and 73.88% at the distal region. However, pathway that make use of K-Means performed best in diaphysis region. This method has obtained the highest accuracy and Dice-coefficient of 99.98% and 95.59% respectively.

An improved retinal blood vessel segmentation for diabetic retinopathy detection

Computer methods in biomechanics and biomedical engineering. Imaging & visualization, Nov 22, 2017

Abstract Diabetic Retinopathy (DR) is an eye disorder that has progressively grows towards people... more Abstract Diabetic Retinopathy (DR) is an eye disorder that has progressively grows towards people who suffered from diabetes. The complications in diabetes cause the damage of blood vessel at the back of the retina. In extreme cases, DR may lead to vision loss or blindness. However, this serious effect was able to be in control through timely treatment and early detection. Recently, this issue is spreading rapidly especially in working area which eventually forced the demand of diagnosis of this illness from the earliest stage. Hence, the detection of retinal blood vessel plays significant role in controlling the progressions of this illness. The advance stages of DR such as neovascularisation which leads to the growth of abnormal vessel can be controlled by extraction of retinal vessel. Therefore, the aim of this research is to develop an approach for blood vessel detection. The proposed method comprises several techniques namely contrast enhancement, background exclusion, filtration, h-maxima transform, multilevel thresholding and morphological operation. The performance of the algorithm was evaluated based on True Positive Fraction, False Positive Fraction and accuracy which achieved the result of 0.9779, 0.0408 and 0.9686, respectively.

Journal of Telecommunication, Electronic and Computer Engineering, May 30, 2018

Microaneurysms (MAs) are the tiny aneurysms which show the earliest sign of diabetic retinopathy ... more Microaneurysms (MAs) are the tiny aneurysms which show the earliest sign of diabetic retinopathy (DR). MAs might progress and harm human eyes if not treated. This paper presents an automatic method for segmentation of MAs in order to control the progression of DR. MESSIDOR database of 40 random images were utilised for further processing. The proposed approach covered pre-processing steps, contrast enhancement, filtration and segmentation by h-maxima transform and multilevel thresholding. Some post-processing techniques also involved in this approach using morphological operation. The detected MAs determined the grade of disease severity. The result showed that the percentage of severity disease detected was 60%.

International Journal on Robotics Automation and Sciences, Sep 29, 2023

Prostate cancer is a significant health concern, ranking as the third most common cancer in Malay... more Prostate cancer is a significant health concern, ranking as the third most common cancer in Malaysian men, with increasing incidence in Asia. The importance of automating the prostate cancer classification process lies in its potential to significantly improve diagnostic accuracy, reduce subjectivity, and enhance overall efficiency compared to the manual approach. The objective of this thesis is twofold: firstly, to effectively enhance and segment crucial features in the images to aid in the classification process, and secondly, to implement a binary classification task that indicates the presence or absence of malignant tissue on histopathology images. The study compares the performance of two image enhancement approaches, stain normalization with adaptive histogram equalization (AHE) and sharpening, and stain normalization with traditional histogram equalization (HE) and sharpening. Additionally, three machine learning models, namely SVM, DenseNet121, and InceptionResNetV2, are implemented and evaluated for prostate cancer binary classification. The findings reveal that AHE contributes to better contrast enhancement and image quality preservation. Moreover, the InceptionResNetV2 model demonstrates superior performance in terms of accuracy (97.25%), sensitivity (97.5%), specificity (97.5%), and area under the curve (AUC) (97.5%).

Image Correction Based on Homomorphic Filtering Approaches: A Study

Image enhancement is an important topic in image analysis in order to help humans and computer vi... more Image enhancement is an important topic in image analysis in order to help humans and computer vision algorithms to obtain an accuracy information for analysis. The visual quality and certain image properties, such as brightness, contrast, signal to noise ratio, resolution, edge sharpness, and colour accuracy were improved through the enhancement process. In this paper, a comprehensive study of image enhancement based on spatial domain (Homomorphic Filtering) 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.

IOP conference series, Jun 1, 2019

Image enhancement is an important topic in image analysis in order to help humans and computer vi... more Image enhancement is an important topic in image analysis in order to help humans and computer vision algorithms to obtain an accuracy information for analysis. The visual quality and certain image properties, such as brightness, contrast, signal to noise ratio, resolution, edge sharpness, and colour accuracy were improved through the enhancement process. The goal of image enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. In this paper, an image enhancement method based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT) was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.

Performance Comparison Using Thresholding Based Method for Diabetic Retinopathy

Patients with diabetes need annual screening to circumvent vision loss which may lead to blindnes... more Patients with diabetes need annual screening to circumvent vision loss which may lead to blindness. Diabetic Retinopathy (DR) is a diabetic complication that causes structural changes in the retina. Non proliferative diabetic retinopathy (NPDR) is a common, usually mild form of retinopathy that generally does not interfere with vision. However, the diabetic retinopathy can progress from non-proliferative to proliferative retinopathy (PDR) if left untreated. To prevent this situation, the automatic computer system is introduced to identify the early stages of DR. There are a lot of studies and research of DR but yet to achieve the accurate result. In order to achieve the target, numerous image segmentation methods were used for comparison performance. In this paper, three datasets namely of DRIVE, E-Optha and Messidor were used as input images. There are three methods from thresholding-based category were used in order to identify the microaneurysms (MAs) and the blood vessel. For DRIVE database, Otsu obtained an accuracy of 92.09%, 93.38% in sensitivity followed by specificity of 64.82%. While Entropy method obtained an accuracy of 92.03%, 94.65% in term of sensitivity followed by 62.38% in specificity. For Fuzzy C Mean (FCM) the accuracy was 92.42%, 94.46% in term of sensitivity and 63.09% in specificity.