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Abstract: We proposed an expert system based on the interpretation of mammographic and ultrasound images that may be used by expert and non-expert doctors in the interpretation and classifying of patient cases. The expert system software consists of a mammographic(MAMMEX) and breast ultrasound(SOUNDEX) medical expert systems which may be used to deduce cases according to the Breast Imaging Recording and Data System (BI-RADS) based upon patients' history, physical and clinical assessment as well as mammograms and breast ultrasound images. The systems were tested on a total of 179 retrospective cases from the Radiology Department, Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian, Kelantan. The accuracy, sensitivity and specificity of MAMMEX were 97%, 96% and 92% and values of 99%, 98% and 100% were found for SOUNDEX respectively. Key Words: Expert Systems, Knowledge-Based Systems, Mammograph, Ultrasound, Breast Cancer, INTRODUCTION Breast cancer is the most common form of malignant disease amongst women. However, mortality rates fell noticeably especially in the United Kingdom partly because of the widespread practice of [...]

Robotic maze pathfinding problems deal with detecting the correct route from the start point to the end-point in a virtual maze environment consisting of walls. Automated robot mobility is a significant feature, which enables a mobile robot to traverse a maze independently, from one position to another, without human intervention. There is a myriad of autonomous industrial mobile robot applications, including the transportation of goods and parts, domestic cleaning, indoor security surveillance, airport baggage couriering, and a plethora of other applications to traverse dangerous locations. This paper proposes a pathfinding mobile robot in a virtual maze based on a combination of a simplified left-hand algorithm and a line-following control algorithm. The mobile robot works in any maze to determine a route from the initial starting point to the end-point. The approach outlined in this paper uses a left-hand algorithm to solve the maze problem and a line-follower control algorithm to enable the robot to move in a straight line through the virtual maze. The algorithm used is less complicated and prevents the robot from falling into infinity loops compared to the traditional wall-follower algorithm. [ABSTRACT FROM AUTHOR]

Image enhancement techniques for digital mammograms can preserve the contrast to distinguish the diagnostic features such as masses and microcalcifications. However, mammogram images suffer low contrast and high image noise due to low exposure radiation used. As a result, the diagnostic features are difficult to detect and analyse by the radiologist. In this article, we propose a novel Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL) enhancement technique to reduce the noise of the mammogram images while preserving contrast and brightness. The FADHECAL technique also applies Fuzzy Clipped Inference System (FCIS) which automatically select clip-limit during the enhancement process. The mammogram images were retrieved from Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) from the University of South Florida database. The results have shown that FADHECAL has the most superior results among other selected enhancement techniques with 6.502 ± 1.855 of AMBE, 0.934 ± 0.034 of SSIM, 15.742 ± 1.217 of MAE, 26.843 ± 2.541 of PSNR, 0.969 ± 0.021 of UIQI and 1.151 ± 0.147 of RMSE values. This FADHECAL can be used as an ideal platform of image enhancement for mammogram images to detect the breast cancer lesions with better noise reduction and preserving the image details. [ABSTRACT FROM AUTHOR]

Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k‐means, fuzzy c‐means, and hierarchical clustering algorithms). The proposed models were used to predict both tilt and orientation angles by two different data sets (month, day, and time variables data set) and (month, day, time, Isc, Voc, and power radiation variables data sets). The results revealed that the proposed MLP/CMLP‐LR systems are able to increase the prediction rate and decrease the mean square error rate as compared to conventional models in both single and dual axis solar tracking systems. The new developed intelligent systems achieved less number of overall connections, less number of neurons, and less time complexity. The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real‐world applications (i.e., solar heating systems, solar lightening systems, factories, and solar powered ventilation. [ABSTRACT FROM AUTHOR]

Summary: Different solar tracking variables have been employed to build intelligent solar tracking systems without considering the dominant and optimum ones. Thus, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent predictors to drive the solar trackers. This research aims to investigate and evaluate the most effective and dominant variables on dual‐ and single‐axis solar trackers and to find the appropriate combination of solar variables and intelligent predictors. The optimum variables will be found by using correlation results between different variables and both orientation and tilt angles. Then, to use the selected variables to develop different intelligent solar trackers. The results revealed that month, day, and time are the most effective variables for horizontal single‐axis and dual‐axis solar tracking systems. Using these variables in cascade multilayer perceptron (CMLP) and multilayer perceptron (MLP) produced high performance. These predictors could predict both orientation and tilt angles efficiently. It is found that day variable is very effective to increase the performance of solar trackers although day variable is neither correlated nor significant with both orientation and tilt angles. Linear regression predicted less than 70% of the given data in most cases, whereas nonlinear models could predict the optimum orientation and tilt angles. In single‐axis tracker, month, day, and time variables achieved prediction rates of 96.85% and 96.83% for three hidden layers of MLP and CMLP, respectively, whereas the MSE are 0.0025 and 0.0008, respectively. In dual‐axis solar tracker, MLP and CMLP predicted 96.68% and 97.98% respectively, with MSE of 0.0007 for both. [ABSTRACT FROM AUTHOR]

The primary role of the oily secretion (meibum) is to ensure tear film stability and retard evaporation. In addition to these is providing ocular surface lubrication, which is necessary for smooth eyelid movements. When a gland is blocked, it is described as a Meibomian gland cyst (MGC), which can be a meibomian cyst, usually referred to as chalazion (eye bump), or in the case of inflammation, it is considered to be a hordeolum (sty or stye). Topical ophthalmic ointments and eyelid heat massages can treat early diagnosed MGC; otherwise, surgical operation is required. The current techniques of diagnosing MGC are usually uncomfortable or invasive, such as examining the tarsal plate after everting the eyelid or by biopsy procedures. The purpose of this work is to propose a non-invasive MGC evaluation and classification technique using hyperspectral imaging and image processing. The proposed technique was carried out on a single patient (i.e., case study) for a period of 4 months to monitor the MGC evolution until postsurgery-recovery and was compared with a normal eyelid patient. The collected hypercube data were processed using Multivariate Curve Resolution (MCR) and image analysis to classify the MGC severity levels. The proposed work built the threshold of the complete system, where early diagnosis of an MGC is possible before it becomes visible to the eye, hence reducing the treatment duration and avoiding any further complications that require clinical lancing. Also, the system can be used as a postoperative check postsurgery to make sure the eyelid went back to normal. [ABSTRACT FROM AUTHOR]

Pap test involves searching of morphological changes in cervical squamous epithelial cells by pathologists or cytotechnologists to identify potential cancerous cells in the cervix. Nuclear membrane irregularity is one of the morphological changes of malignancy. This paper proposes two novel techniques for the evaluation of nuclear membrane irregularity. The first technique, namely, penalty-driven smoothing analysis, introduces different penalty values for nuclear membrane contour with different degrees of irregularity. The second technique, which can be subdivided into mean- or median-type residual-based analysis, computes the number of points of nuclear membrane contour that deviates from the mean or median of the nuclear membrane contour. Performance of the proposed techniques was compared to three state-of-the-art techniques, namely, radial asymmetric, shape factor, and rim difference. Friedman and post hoc tests using Holm, Shaffer, and Bergmann procedures returned significant differences for all the three classes, i.e., negative for intraepithelial lesion or malignancy (NILM) versus low grade squamous intraepithelial lesion (LSIL), NILM versus high grade squamous intraepithelial lesion (HSIL), and LSIL versus HSIL when the span value equaled 3 was employed with linear penalty function. When span values equaled 5, 7, and 9, NILM versus LSIL and HSIL showed significant differences regardless of the penalty functions. In addition, the results of penalty-driven smoothing analysis were comparable with those of other state-of-the-art techniques. Residual-based analysis returned significant differences for the comparison among the three diagnostic classes. Findings of this study proved the significance of nuclear membrane irregularity as one of the features to differentiate the different diagnostic classes of cervical squamous epithelial cells. [ABSTRACT FROM AUTHOR]

In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a study of the usage of intensity exposure in histogram segmentation and its performance in histogram equalization. Two techniques are proposed: the mean-based bi-histogram equalization plateau limit (mean-BHEPL) or median-based BHEPL (median-BHEPL) and adaptive bi-histogram equalization algorithm (ABHE). Both techniques initially divide the input histogram into two subhistograms through a threshold value computed from the intensity exposure of the image. Histogram clipping for mean-BHEPL and median-BHEPL is then performed on these subhistograms using the mean and median values, respectively. Conventional histogram equalization is also implemented on each clipped subhistogram. The second proposed technique, ABHE, applies the modified version of the adaptive histogram equalization algorithm (AHEA) on both subhistograms. Results of extensive simulations reveal that mean-BHEPL and median-BHEPL perform comparably to the conventional BHEPL technique. ABHE exhibits excellent performance in image quality, naturalness, and mean brightness preservation. However, it is slightly inferior in image detail preservation to the conventional AHEA technique. In conclusion, segmenting the input histogram through the threshold value calculated based on the intensity exposure of the image yields good enhancement results. [ABSTRACT FROM AUTHOR]