Khairunnisa Hasikin

Enhancement of the Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image. Most of the developed contrast enhancement techniques improved image quality without considering the nonuniform lighting in the image. The fuzzy grayscale image enhancement technique is proposed by maximizing fuzzy measures contained in the image. The membership function is then modified to enhance the image by using power-law transformation and saturation operator. The qualitative and quantitative performances of the proposed method are compared with the other methods. The proposed method produced better quality enhanced image and required minimum processing time than the other methods.

Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images

This paper presents a new enhancement technique using the fuzzy set theory for low contrast and nonuniform illumination images. A new parameter called the contrast factor which will provide information on the difference among the gray-level values in the local neighborhood is proposed. The contrast factor is measured by both local and global information to ensure that the fine details of the degraded image are enhanced. This parameter is used to divide the degraded image into bright and dark regions. The enhancement process is applied on gray-scale images wherein the modified Gaussian membership function is employed. The process is performed separately according to the image’s respective regions. The performance of the proposed method is comparable with other state-of-the-art techniques in terms of processing time. The proposed method exhibits the best performance and defeats other methods in terms of preserving brightness and details without amplifying existing noises.

Fuzzy image enhancement for low contrast and non-uniform illumination images

This paper presents the fuzzy image enhancement for low contrast and non-uniform illumination images. A new fuzzy intensity measure is proposed to distinguish between the dark and bright regions. This measure is computed by considering the average intensity and deviation of the intensity distribution of the image. The input image is enhanced using a power-law transformation. Implementation of the proposed algorithm on the non-uniform illumination and low contrast images show that the proposed algorithm outperforms the other enhancement techniques. The proposed algorithm produces more even illumination with improvement in the details and contrasts. In addition, the proposed algorithm is computationally fast to be implemented in real time application.

Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitatively and quantitatively compared with the conventional clustering algorithms, namely, the K-Means (KM), Fuzzy C-Means (FCM), Moving K-Means (MKM), and Adaptive Fuzzy K-Means (AFKM) algorithms. The A2DKM outperforms these algorithms by producing more homogeneous segmentation results.

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

Abstract. Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.

Fuzzy enhancement for nonuniform illumination of microscopic Sprague Dawley rat sperm image

This paper presents a fuzzy grayscale enhancement technique for nonuniform illumination of microscopic Sprague Dawley rat sperm image. The microscopic images extracted from the sperm motility analysis video are low in contrast and having nonuniform lighting. Most of the developed techniques enhanced the microscopic image without considering nonuniform brightness in the image. Thus, overenhanced or underenhanced phenomena in the processed image are inevitable. The fuzzy grayscale image enhancement technique is proposed to overcome the aforementioned problems. The enhancement process of sperm images is conducted according to predetermined overexposed and underexposed regions. The proposed method has attained optimum fuzziness measures and the quality of the sperm image is improved. In addition, the proposed method required minimum processing time as compared to the other methods.

Determination of Design Parameters of a Biosensor for Human Artery Pulse Wave Detection

This paper describes the modeling of a microdiaphragm of a human artery biosensor by varying diaphragm’s diameter and thickness. Both parameters are varied to evaluate their effects on diaphragm’s deflection and pressure sensitivity. The sensor used circular silicon nitride diaphragm as a pressure sensing element. The findings indicate that the diaphragm’s thickness should be small whereas the diameter should be large enough in order to achieve high sensitivity. The simulation results demonstrate that the sensor has reasonable linearity and sensitivity for the measurement ranges from 0 to 300mmHg.

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the first step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To differentiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To confirm the system’s performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and specificity of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

Modeling of a polyimide diaphragm for an optical pulse pressure sensor

This paper presents the modeling of a polyimide diaphragm for an optical pulse pressure sensor. Polyimide is a type of polymer materials that possessed low linear coefficient of thermal expansion and has good thermal stability. The polyimide diaphragm has been designed and its performance is analyzed in terms of diaphragm deflection, diaphragm pressure sensitivity and diaphragm resonance frequency. Two design parameters namely diaphragm radius and diaphragm thickness are varied to study the diaphragm performance. It can be concluded that the modeled micro-diaphragm with a diaphragm radius of 90µm and diaphragm thickness of 4µm respectively has satisfied the maximum allowable deflection and operated in optimum frequency response.

Feature-Based Retinal Image Registration Using D-Saddle Feature

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.