Wan Azani Mustafa

Illumination normalization of non-uniform images based on double mean filtering

In segmentation process, non-uniform illumination problem can affect the segmentation result. In this paper, a new method is proposed to solve the problem based on double mean filtering. By applying a combination between mean and threshold value, the varying background is normalized. This proposed method had been experimented with a few badly illuminated images and the result is evaluated by using Misclassification Error (ME), Sensitivity and Specificity. Based on the ME results, proposed method increases the segmentation correction to 88.27%. Besides that, the sensitivity and specificity of proposed method obtained is 94.56190% and 98.57924% and for classical Otsu is 90.30550% and 61.85435%.

Conversion of the Retinal Image Using Gray World Technique

Retinal images are routinely acquired and assessed to provide diagnostic for many important diseases like diabetic retinopathy. People with proliferative retinopathy can reduce their risk of blindness by 95 percent with timely treatment and appropriate follow-up care. The color constancy is used in this context to define the ability of the visual system to estimate an object color transmitting an unpredictable spectrum to the eyes. In this paper, a Gray World method was proposed by assuming the average of the surface reflectance of a typical scene is some pre-specified value. The main idea based on illumination estimated using the statistical region data. The effectiveness of the Gray Word method and normal gray technique was calculated by using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The Gray World achieved the highest PSNR and lowest MSE proved that the image quality was improved. The proposed method can be used to help the ophthalmologist to detect a lesion in the retinal image automatically. Through the contrast variation in retinal images, the disease can be recognized very well.

A review: Comparison between different type of filtering methods on the contrast variation retinal images

Retinal images are obtained using a fundus camera in order to evaluate many important diseases such as Diabetic Retinopathy and Glaucoma. Sometimes, the images appear as uniformly illuminated, have luminosity and contrast variability. This problem can be severely compromised the diagnostic process and the results, especially if automated computer-based procedure is used to derive the parameters of diagnostic. Many researchers propose different approaches to normalize the badly illuminated images based on filtering techniques. In this paper, we compare a six (6) type of filtering techniques and applied to the retinal images from Digital Retinal Images for Vessel Extraction (DRIVE) database to adjust the contrast variation and illumination in order to produce a better diagnostic result. The result performance is evaluate based on Signal Noise Ratio (SNR) and Mean Square Error (MSE) is compared to the other filtering methods. From the result, the Homomorphic filtering based on high pass filter obtained higher SNR value which is 3.093 and the lowest in MSE which is 71267.51.

Luminosity Correction Using Statistical Features on Retinal Images

Retinal fundus image is important for the ophthalmologist to identify and detect many vision-related diseases, such as diabetes and hypertension. From an acquisition process, retinal images often have low gray level contrast and low dynamic range. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. In this paper, a new proposed method based on statistical information such as mean and standard deviation was studied. The combination of local and global technique was successful to detect the luminosity region. Then, a simple correction intensity equation was proposed in order to replace the problem intensity. The results of the numerical simulation (SNR = 2.347 and GCF = 4.581) indicate that the proposed method effective to enhance the luminosity region. Implications of the results and future research directions are also presented. Keywords: Detection, Luminosity, Retinal, Statistical.

Combination of Gray-Level and Moment Invariant for Automatic Blood Vessel Detection on Retinal Image

Segmentation of blood vessels in the retinal is a crucial step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. This paper presents a supervised method for automatic segmentation of blood vessels in retinal images. The proposed method based on a hybrid combination between Gray-Level and Moment Invariant techniques. There are four steps involved, whereas preprocessing, feature extraction, classification, and post-processing. In the preprocessing, three stages are performed include vessel central light reflex removal, background homogenization, and vessel enhancement. The 7-D vector feature extraction was performed to compute that compose of gray-level and moment invariants-based features for pixel representation. The decision tree is used for classification step that characterized the pixel based on vessels and non-vessels. The final step is the post-processing which will remove the small artifacts appears after classification process. The proposed method was compared to the Vascular Tree method and Morphological method. Based on the objective evaluation, the proposed method achieved (sensitivity = 98.589, specificity = 55.544 and accuracy = 96.197).

Binarization of Document Images: A Comprehensive Review

Document image binarization is one important pre-processing step, especially for data analysis. Extraction of text from images and its recognition may be challenging due to the presence of noise and degradation in document images. In this paper, seven (7) types of binarization method were discussed and tested on Handwritten Document Image Binarization Contest (H-DIBCO 2012). The aim of this paper is to provide comprehensive review methods in order to binary document images in the damaging background. The results of the numerical simulation indicate that the Gradient Based method most effective and efficient compared to other methods. Hopefully, the implications of this review give future research directions for the researchers.

Document Image Database (2009 - 2012): A Systematic Review

Document image binarization contributes significantly to the success of the document image analysis and recognition challenging tasks. Image quality can play an important role in addressing the issue of binarization effectiveness. In this paper, a comprehensive review of document database was presented. Review based on image from Document Image Binarization Contest (DIBCO) 2009 to 2012 consists handwritten and printed image. The best algorithm for each year is discussed and analysed. Implications of the review give the direction for future binarization approach developments.

A Novel Contrast Enhancement Technique Based on Combination of Local and Global Statistical Data on Malaria Images

Malaria appears to be one of the main reasons for detrimental health issue at the global scale that is responsible for approximately half a million deaths every year. As the cases of malaria seem to escalate at an annual rate, it is vital to provide a rapid and accurate diagnosis through manual microscopic assessment in the attempt to control the spread of malaria. Nevertheless, varied staining steps and noise disruptions can cause inaccurate diagnosis due to wrong interpretation. Hence, to address such issues, this study investigated the performance upon removing background noise and the method of correcting illumination that has an impact upon segmentation for a computer-assisted diagnostic system. The findings display that the technique of based on Otsu threshold and statistic data used to enhance the contrast image as to determine cells infected by the malaria parasite, in comparison to other methods. In fact, this method was tested on 450 malaria images, which consisted of P. Vivax, P. Falciparum, and P. Knowlesi species at the stages of trophozoite, schizont, and gametocyte. As a result, the HSE approach yielded 1.31 for Global Contrast Factor (GCF), while 10.56 for Signal Noise Ratio (SNR).