A local tone mapping operator for high dynamic range images

In this paper, we present a new tonemapping operator to display high dynamic range image onto conventional displayable devices and printers. In our work, a new tone map algorithm, derived from the Contrast Limited Adaptive histogram Equalization (CLAHE) technique is presented. Due to different luminance intervals could result in overlapped reaction on the limited response in limited response range of visual system, we use scenes region splitting and merging to segment the scaled luminance, L(x, y) and perform the CLAHE in each segment with different clip limit in order to extending our visual response range to cope with the full dynamic range of high contrast. Until now, there is no fix standard of objective evaluation available to measuring the quality of displayed High Dynamic Range (HDR) images because it is difficult to know how the light or dark the image should be displayed to faithful to the original HDR image. As the result, the main evaluation is based on human's subjective evaluation. In this paper, we consider this to evaluate the performances with different tone mapping method

A new technique to reproduced high-dynamic-range images for low-dynamic-range display

Tone mapping is a process for reproduction of High-Dynamic-Range images (HDR) for Low-Dynamic-Range (LDR) output devices. In this report, author presents a new local tone-mapping operator, derived from the Contrast Limited Adaptive histogram Equalization (CLAHE) technique for displaying high dynamic range image. The CLAHE is a method which was originally developed for medical imaging. This method has effectively expanded the full dynamic range of display and it is fully automatic. Due to different luminance intervals could result in overlapped reaction on the limited response in limited response range of visual system, scene region splitting and merging were used to segment the scaled luminance and perform the image segmentation to segment image into smaller part. After the region splitting and merging, there will be some noise or variation of intensity that may result in holes or over segmentation. As the result, the morphological operation, opening and closing were used to perform the mask to applied different clip limit of the CLAHE operation

The thermoluminescence response of doped SiO2 optical fibres subjected to fast neutrons

This paper describes a preliminary study of the thermoluminescence (TL) response of doped SiO2 optical fibres subjected to 241AmBe neutron irradiation. The TL materials, which comprise Al- and Ge-doped silica fibres, were exposed in close contact with the 241AmBe source to obtain fast neutron interactions through use of measurements obtained with and without a Cd filter (the filter being made to entirely enclose the fibres). The neutron irradiations were performed for exposure times of 1-, 2-, 3-, 5- and 7-days in a neutron tank filled with water. In this study, use was also made of the Monte Carlo N-particle (MCNPTM) code version 5 (V5) to simulate the neutron irradiations experiment. It was found that the commercially available Ge-doped and Al-doped optical fibres show a linear dose response subjected to fast neutrons from 241AmBe source up to seven days of irradiations. The simulation performed using MCNP5 also exhibits a similar pattern, albeit differing in sensitivity. The TL response of Ge-doped fibre is markedly greater than that of the Al-doped fibre, the total absorption cross section for Ge in both the fast and thermal neutrons region being some ten times greater than that of Al

Random walkers based segmentation method for breast thermography

In breast thermography diagnostic, proper detection and segmentation of the areola area as well as detection of breast boundaries present the biggest challenge. As the boundaries of breasts especially in the upper quadrants are usually not present, this produces a great deal of challenge to segment breasts automatically. Many approaches have been developed to segment the breast in the past such as Snakes, Active Contours and Circular Hough Transforms, but these methods fail to detect the boundaries of the breast with the required level of accuracy especially the upper boundaries of the breast. By utilizing most recent segmentation method which is Random Walkers, the breast can be segmented accurately which in turn will increase the accuracy and the reliability of computer aided detection/diagnosis systems

Detection of denial of service attacks against domain name system using neural networks

In this paper we introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). Our system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a shorttime frame from traffic received by the target name server. We compared three different neural networks for detecting and classifying different types of DoS attacks. The proposed system is evaluated in a simulated network and showed that the best performed neural network is a feed-forward backpropagation with an accuracy of 99%

Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery

Plantation inventory and management require a range of fine-scale remote-sensing data. Remote-sensing images with high spatial and spectral resolution are an efficient source of such information. This article presents an approach to the extraction and counting of oil palm trees from high spatial resolution airborne imagery data. Counting oil palm trees is a crucial problem in specific agricultural areas, especially in Malaysia. The proposed scheme comprises six major parts: (1) discrimination of oil palms from non-oil palms using spectral analysis, (2) texture analysis, (3) edge enhancement, (4) segmentation process, (5) morphological analysis and (6) blob analysis. The average accuracy obtained was 95%, which indicates that high spatial resolution airborne imagery data with an appropriate assessment technique have the potential to provide us with vital information for oil palm plantation management. Information on the number of oil palm trees is crucial to the ability of plantation management to assess the value of the plantation and to monitor its production

Grid base classifier in comparison to nonparametric methods in multiclass classification

In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification

Automatic liver segmentation on computed tomography using random walkers for treatment planning

Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers . To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95% and dice similarity coefficient of 0.91

Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset

Brain anatomical variations among Malaysian population

Spatial normalization is an important task in neuroimaging analysis to compensate with the anatomical variations between different subjects. Current practice is to normalize the sample image to the reference brain atlas to ensure that the unbiased image-to-image comparison is possible. The available template nowadays is ICBM152, which derived from a Caucasian population. Since the brain anatomical variations has been reported especially for the inter-regional cohorts, the use of the ICBM152 template for other region subjects is questionable. In addition to that, several other factors such as age and gender have also been reported to have an effect to the brain morphometric. This study investigates the global brain shape measures for different group among Asian population. It is useful as a basis for further investigation on a more complex brain structure differences among local and inter-regional population. Later on, a group-specific atlas may be constructed to be used exclusively for Asian subjects. This study showed that gender factor has a significant effect on the brain shape while the age and race demonstrated no significant correlation