Advent of medical image digitalization leads to image processing and computer-aided diagnosis systems in numerous clinical applications. These technologies could be used to automatically diagnose patient or serve as second opinion to pathologists. This paper briefly reviews cervical screening techniques, advantages, and disadvantages. The digital data of the screening techniques are used as data for the computer screening system as replaced in the expert analysis. Four stages of the computer system are enhancement, features extraction, feature selection, and classification reviewed in detail. The computer system based on cytology data and electromagnetic spectra data achieved better accuracy than other data.

This paper investigated the effects of critical-point drying (CPD) and hexamethyldisilazane (HMDS) sample preparation techniques for cervical cells on field emission scanning electron microscopy and energy dispersive X-ray (FE-SEM/EDX). We investigated the visualization of cervical cell image and elemental distribution on the cervical cell for two techniques of sample preparation. Using FE-SEM/EDX, the cervical cell images are captured and the cell element compositions are extracted for both sample preparation techniques. Cervical cell image quality, elemental composition, and processing time are considered for comparison of performances. Qualitatively, FE-SEM image based on HMDS preparation technique has better image quality than CPD technique in terms of degree of spread cell on the specimen and morphologic signs of cell deteriorations (i.e., existence of plate and pellet drying artifacts and membrane blebs). Quantitatively, with mapping and line scanning EDX analysis, carbon and oxygen element compositions in HMDS technique were higher than the CPD technique in terms of weight percentages. The HMDS technique has shorter processing time than the CPD technique. The results indicate that FE-SEM imaging, elemental composition, and processing time for sample preparation with the HMDS technique were better than CPD technique for cervical cell preparation technique for developing computer-aided screening system.

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.

In Malaysia, the screening coverage for cervical cancer is poor, which was at 2% in 1992, 3.5% in 1995, and at 6.2% in 1996, due to the shortage in pathologist workforce being one of the major cause. Study has been done before to overcome this by developing a diagnosis system based on neural networks, so that diagnosis can be done by an automated system with pathologist-like knowledge. Cell's features were used as input to the neural network architecture, and cell's classification into NORMAL, Low-Squamous Intraepithelial Lession (LSIL), or High-Squamous Intraepithelial Lession (HSIL) were used as output target. This paper focused on finding the best neural network to be used as classifier tool for cervical cancer diagnostic system with cervical cells' features as input. Two architectures of neural network system were proposed; Cascaded Multilayered Perceptron (c-MLP) and Extreme Learning Machine (ELM). Result suggests that all the features selected which are area, grey level, perimeter, red, green, blue, intensity1, intensity2 and saturation are more suitable to be used with c-MLP neural network architecture compared to ELM, with the accuracy of 96.02%.

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications.