Mohd Fadzil bin Abdul Kadir

Extraction Method of Retinal Border Lines in Optical Coherence Tomography Image by Using Dynamic Contour Model

In the field of ophthalmology, the needs of retina diagnosis using optical coherence tomography (OCT) images have been growing, and the automatic measurement of a retina thickness and its quantitative evaluation are desired for the diagnosis of retinal diseases. Previously, the automatic measurement methods of the retinal thickness have been reported for retinal OCT images. These previous methods can extract the retinal border lines (ILM and RPE) appropriately in most cases of normal OCT image. However these methods caused the tracking error to some OCT images with large noises. In this paper, we propose a new automatic measurement method of a retinal thickness in OCT image. The method employs ODAN (One Directional Active Net) to extract ILM and RPE. ODAN employs a new energy function to extract the retinal border lines exactly and all nodes of ODAN moves only to one direction to minimize the total energy repeatedly. The energy function consists of (1) the conformity characteristics energy of image and (2) the internal strain energy. We confirmed the usefulness of the ODAN by the experimental results for ten OCT images with large noises. We compared the positions of retinal border lines by the proposed method with the positions in a manual trace by ophthalmology specialist. In the comparative result, the proposed method is useful as the basic method for the detection of retinal diseases.

A Retinal Layer Structure Analysis to Measure the Size of Disease Using Layer Boundaries Detection for Optical Coherence Tomography Images

In the field of ophthalmology, optical coherence tomography (OCT) is rapidly becoming popular in clinical applications to diagnose retinal disease. In this paper, we proposed a new profile analysis to evaluate the size of the retinal disease using the number of layer boundaries. The number is established by a new analysis method of a gray level profile scanned in longitudinal direction for an OCT image. We employed the proposed method for 50 OCT images of normal retina and 50 OCT images of abnormal retina. The experiment result showed that a significant difference was obtained in the significance level at 1%, when we employed Mann-Whitney U method on the standard deviation of the number of layer boundaries for normal and abnormal retinal images group. Therefore, we confirmed that the proposed method becomes one of the indexes to evaluate the size of the retinal disease. In addition, we confirmed that our system can measure the size of abnormal part in horizontal direction using the number of layer boundaries.

Extraction of Disease Area from Retinal Optical Coherence Tomography Images Using Three Dimensional Regional Statistics

We propose a new extraction method of the macular disease area in the human retinal layer from OCT images using three dimensional regional statistics. In previous researches, we extracted disease area by using the mean and standard deviation of the two dimensional disease part pointed out by a clinical doctor. However, the previous method cannot extract disease area for some disease OCT images precisely. In this paper, we propose a new extraction method of the disease area using three dimensional regional statistics. We use a set of 128 images (3D-OCT image) consisted of 2 dimensional OCT retinal image about one retina of a patient. The regional mean and regional standard deviation of gray level are calculated in the three dimensional region of interest (ROI, 125 (=5 × 5 h 5) pixels) in the abnormal area pointed by a clinical doctor. These values are compared with every ROI in the abnormal area to extract the disease area, and the proposal system measures the volume of the disease area. We apply the proposed method to OCT images of 5 patients with retinal diseases. As a result, we can measure the volume of the abnormal area with 80.7% average accuracy.

Noise removal using statistical operators for efficient leaf identification

Plant identification based on leaf shape is becoming a popular trend, since each leaf carries substantial information that can be used to identify plant. This is difficult because the features of a leaf shape can be influenced by other leaves that have similar features but different categories. This paper presents the most popular statistical operators: mean filtering technique (MFT), median filtering technique (MDFT), Wiener filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for enhancing preprocessing stage. The performance of these techniques was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). Ten features were extracted from the pre-processed leaf images and identification performance was also evaluated using precision and recall. It is found that WFT is the best filtering technique and gives the best identification accuracy of 95.1%.

Determination of mango fruit from binary image using randomized Hough transform

A method of detecting mango fruit from RGB input image is proposed in this research. From the input image, the image is processed to obtain the binary image using the texture analysis and morphological operations (dilation and erosion). Later, the Randomized Hough Transform (RHT) method is used to find the best ellipse fits to each binary region. By using the texture analysis, the system can detect the mango fruit that is partially overlapped with each other and mango fruit that is partially occluded by the leaves. The combination of texture analysis and morphological operator can isolate the partially overlapped fruit and fruit that are partially occluded by leaves. The parameters derived from RHT method was used to calculate the center of the ellipse. The center of the ellipse acts as the gripping point for the fruit picking robot. As the results, the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.

Personal Dictionaries for Handwritten Character Recognition Using Characters Written by a Similar Writer

We propose two new generation methods of personal dictionary for handwritten character recognition using the set of characters written by a similar writer. The methods employ only one character written by one specific writer, and its character selects the set of characters written by the similar writer to generate the personal dictionary for the specific writer. The first type (similar mean) dictionary uses the mean feature vector of a similar writer which is selected by only one specific writer’s character. The second type (similar feature space) dictionary uses the mean feature vector and the covariance matrix of the selected similar writer. We compared the effect for handwritten Japanese “HIRAGANA” characters. The similar feature space dictionary obtained the recognition rate 91% relatively to the rate of a general dictionary 82 %. It is confirmed that only one character by a specific writer is very effective on personal character recognition.