Rozi Mahmud

EXPERIMENTAL BREAST TUMOR DETECTION USING NN-BASED UWB IMAGING

This paper presents a system with experimental comple-ment to a simulation work for early breast tumor detection. The ex-periments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms for homogenous and heterogeneous tissues. The proposed breast phantoms (homoge-neous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal e®ort. A speci¯c glass is used as skin. All the materials and their mixtures are con-sidered according to the ratio of the dielectric properties of the breast tissues. Experiments to detect tumor are performed in regular noisy room environment. The UWB signals are transmitted from one side of the breast phantom (for both cases) and received from opposite side diagonally repeatedly. Using discrete cosine transform (DCT) of these received signals, a Neural Network (NN) module is developed, trained and tested. The tumor existence, size and location detection rates for both cases are highly satisfactory, which are approximately: (i) 100%,95.8% and 94.3% for homogeneous and (ii) 100%, 93.4% and 93.1% for heterogeneous cases respectively. This gives assurance of early de- tection and the practical usefulness of the developed system in near future.

Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

OBJECTIVE Boosting accuracy in automatically discriminating patients with Alzheimers disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI). METHOD Longitudinal percentage of brain volume changes (PBVC) in two-year follow up and its intermediate counterparts in early 6-month and late 18-month are used as features in supervised and unsupervised classification procedures based on K-mean, fuzzy clustering method (FCM) and support vector machine (SVM). The most relevant features for classification are selected using discriminative analysis (DA) of features and their principal components (PC). Accuracy of the proposed method is evaluated in a group of 30 patients with AD (16 males, 14 females, age±standard-deviation (SD)=75±1.36 years) and 30 normal controls (15 males, 15 females, age±SD=77±0.88 years) using leave-one-out cross-validation. RESULTS Results indicate superiority of supervised machine learning techniques over unsupervised ones in diagnosing AD and withal, predominance of RBF kernel over lineal one. Accuracies of 83.3%, 83.3%, 90% and 91.7% are achieved in classification by K-mean, FCM, linear SVM and SVM with radial based function (RBF) respectively. CONCLUSION Evidence that SVM classification of longitudinal atrophy rates may results in high accuracy is given. Additionally, it is realized that use of intermediate atrophy rates and their principal components improves diagnostic accuracy.

3d experimental detection and discrimination of malignant and benign breast tumor using nn-based uwb imaging system

This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric properties are mainly controlled by the water and blood content of tumors. For simulation, electromagnetic simulator software is used. The experiment is conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and homogenous breast phantom. The 3D homogeneous breast phantom and tumors are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. The simulation and experimental setups are performed by transmitting the UWB signals from one side of the breast model and receiving from opposite side diagonally. Using discrete cosine transform (DCT) of received signals, we have trained and tested the developed experimental Neural Network model. In 3D breast model, the achieved detection accuracy of tumor existence is around 100%, while the locating accuracy in terms of (x, y, z) position of a tumor within the breast reached approximately 89.2% and 86.6% in simulation and experimental works respectively. For classification, the permittivity and conductivity detection accuracy are 98.0% and 99.1% in simulation, and 98.6% and 99.5% in experimental works respectively. Tumor detection and type specification 3D may lead to successful clinical implementation followed by saving of precious human lives in the near future.

New multi-scale medical image segmentation based on fuzzy c-mean (FCM)

Image segmentation is a key process in computer vision and image process applications. Accurate segmentation of medical images is very essential in medical applications but it is very difficult job due to noise and in homogeneity that are usual of medical images. In this paper a new method, based on FCM, is proposed to make FCM more robust against noise. Multi-scale images are obtained by smoothing input image in different scales. FCM is applied to multi-scale images from high scale to low scale. First FCM is applied to image with highest scale. Then in each scale, cluster centers of previous scale are used to initialization membership for current scale. Moreover, in FCM, neighborhood attraction is used to more decrease effect of noise in clustering. Experimental result shows effectiveness of new method.

Molecular targets in the discovery and development of novel antimetastatic agents: Current progress and future prospects

Tumour invasion and metastasis have been recognized as major causal factors in the morbidity and mortality among cancer patients. Many advances in the knowledge of cancer metastasis have yielded an impressive array of attractive drug targets, including enzymes, receptors and multiple signalling pathways. The present review summarizes the molecular pathogenesis of metastasis and the identification of novel molecular targets used in the discovery of antimetastatic agents. Several promising targets have been highlighted, including receptor tyrosine kinases, effector molecules involved in angiogenesis, matrix metalloproteinases (MMPs), urokinase plasminogen activator, adhesion molecules and their receptors, signalling pathways (e.g. phosphatidylinositol 3‐kinase, phospholipase Cγ1, mitogen‐activated protein kinases, c‐Src kinase, c‐Met kinases and heat shock protein. The discovery and development of potential novel therapeutics for each of the targets are also discussed in this review. Among these, the most promising agents that have shown remarkable clinical outcome are anti‐angiogenic agents (e.g. bevacizumab). Newer agents, such as c‐Met kinase inhibitors, are still undergoing preclinical studies and are yet to have their clinical efficacy proven. Some therapeutics, such as first‐generation MMP inhibitors (MMPIs; e.g. marimastat) and more selective versions of them (e.g. prinomastat, tanomastat), have undergone clinical trials. Unfortunately, these drugs produced serious adverse effects that led to the premature termination of their development. In the future, third‐generation MMPIs and inhibitors of signalling pathways and adhesion molecules could form valuable novel classes of drugs in the anticancer armamentarium to combat metastasis.

Skull stripping of MRI brain images using mathematical morphology

Skull stripping is a major phase in MRI brain imaging applications and it refers to the removal of its non-cerebral tissues. The main problem in skull-stripping is the segmentation of the non-cerebral and the intracranial tissues due to their homogeneity intensities. As morphology requires prior binarization of the image, this paper proposed mathematical morphology segmentation using double and Otsus thresholding. The purpose is to identify robust threshold values to remove the non-cerebral tissue from MRI brain images. Ninety collected samples of T1-weighted, T2-weighted and FLAIR MRI brain images are used in the experiments. The results showed promising use of double threholding as a robust threshold value in handling intensity inhomogeneities compared to Otsus thresholding.

Medical Image Segmentation Using Fuzzy C-Mean (FCM), Learning Vector Quantization (LVQ) and User Interaction

Accurate segmentation of medical images is very essential in medical applications. We proposed a new method, based on combination of Learning Vector Quantization (LVQ), FCM and user interaction to make segmentation more robust against inequality of content with semantic, low contrast, in homogeneity and noise. In the postulated method, noise is decreased using Stationary wavelet Transform (SWT); input image is clustered using FCM to the n clusters where n is the number of target classes, afterwards, user selects some of the clusters to be partitioned again; each user selected cluster is clustered to two sub clusters using FCM. This process continues until user to be satisfied. Then, user selects clusters for each target class; user selected clusters are used to train LVQ. After training LVQ, image pixels are clustered by LVQ. Segmentation of simulated and real images is demonstrated to show effectiveness of new method.

Protective Effects of Nigella sativa on Metabolic Syndrome in Menopausal Women

PURPOSE This study was conducted in menopausal women to determine the metabolic impact of Nigella sativa. METHODS Thirty subjects who were menopausal women within the age limit of 45-60 were participated in this study and randomly allotted into two experimental groups. The treatment group was orally administered with N. sativa seeds powder in the form of capsules at a dose of 1g per day after breakfast for period of two months and compared to control group given placebo. Anthropometric and biochemical parameters were measured at baseline, 1st month, 2nd month and a month after treatment completed to determine their body weight, serum lipid profile and fasting blood glucose (FBG). RESULTS The treatment group showed slight reduction with no significant difference in body weight changes of the respondents. However, significant (p<0.05) improvement was observed in total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and blood glucose (p<0.05). CONCLUSION These results suggested that treatment with N. sativa exert a protective effect by improving lipid profile and blood glucose which are in higher risk to be elevated during menopausal period.

Study on the Potential Toxicity of a Thymoquinone-Rich Fraction Nanoemulsion in Sprague Dawley Rats

Toxicological studies constitute an essential part of the effort in developing an herbal medicine into a drug product. A newly developed thymoquinone-rich fraction nanoemulsion (TQRFNE) has been prepared using a high pressure homogenizer. The purpose of this study was to investigate the potential acute toxicity of this nanoemulsion in Sprague Dawley rats. The acute toxicity studies were conducted as per the OECD guidelines 425, allowing for the use of test dose limit of 20 mL TQRFNE (containing 44.5 mg TQ)/kg. TQRFNE and distilled water (DW) as a control were administered orally to both sexes of rats on Day 0 and observed for 14 days. All the animals appeared normal, and healthy throughout the study. There was no observed mortality or any signs of toxicity during the experimental period. The effects of the TQRFNE and DW groups on general behavior, body weight, food and water consumption, relative organ weight, hematology, histopathology, and clinical biochemistry were measured. All the parameters measured were unaffected as compared to the control (DW) group. The administration of 20 mL TQRFNE /kg was not toxic after an acute exposure.

Monte Carlo Simulation on Breast Cancer Detection Using Wire Mesh Collimator Gamma Camera

This paper presents the preliminary results of the new low energy high resolution wire-mesh collimator gamma camera in mapping breast cancer cells, by employing 140 keV photons of Technetium-99 m radionuclide tracer. The complete model of photons propagation and detection, as well as the human cells activities are simulated using Monte Carlo N-Particle code. Abnormal cells of different tumor to background values are investigated, and the results from the conventional collimator and wire-mesh collimator are compared. The results are evaluated in terms of the collimator sensitivity and the contrast to background ratio. In our assessment, the wire mesh collimator gamma camera yields slightly better results than the multihole collimator for sensitivity, however produces insignificant performance in the contrast to background evaluation.