Cancerous lung nodule detection in computed tomography images
Authors : Ayman Abu Baker, Yazeed Ghadi
Abstract : Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image..
Keywords : cancer detection; computed tomography; lung cancer; texture features; laplacian filter;
EEG Mouse: A Machine Learning-Based Brain Computer Interface
Authors : Mohammad H. Alomari, Ayman AbuBaker, Aiman Turani, Ali M. Baniyounes, Adnan Manasreh
Abstract : The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time.
Keywords : EEG; BCI; Data Mining; Machine Learning; SVMs; NNs; DWT; Feature Extraction
One Scan Connected Component Labeling Technique
Authors : Ayman AbuBaker; Rami Qahwaji; Stan Ipson; Mohmmad Saleh
Abstract : This paper, presents a new component labeling algorithm which is based on scanning and labeling the objects in a single scan. The algorithm has the ability to test the four and eight connected branches of the object. This algorithm, which is fast and requires low memory allocation, can also process an image that contains large numbers of objects. The algorithm is used to scan the image from left to right and from top to bottom to find the unlabeled objects. A comparison analysis is performed with other component labeling algorithms. Our algorithm has shown an outstanding performance with respect to the processing time. A practical application with computer based mammography is also included.
Keywords : Labeling, Signal processing algorithms, Application software, Image storage, Signal processing, Informatics, Pixel, Image coding, Testing, Performance analysis
Efficient Pre-processing of USF and MIAS Mammogram Images
Authors : Ayman A. AbuBaker , R. S. Qahwaji , Musbah J. Aqel , Hussam Al-Osta , Mohmmad H. Saleh
Abstract : High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a preprocessing technique for reducing the size and enhancing the quality of USF and MIAS mammogram images is introduced. The algorithm analyses the mammogram image to determine if 16-bit to 8-bit conversion process is required. Enhancement is applied later followed by a scaling process to reduce the mammogram size. The performances of the algorithms are evaluated objectively and subjectively. On average 87 % reduction in size is obtained with no loss of data at the breast region..
Keywords : mias mammogram image, efficient pre-processing, high computational capability , high quality mammogram image, breast region, mammogram imag,e large size image, high resolution, mammogram size ,, remote radiologist, preprocessing technique , scaling process , 8-bit conversion process
A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique
Authors : Ayman AbuBaker
Abstract : A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study. The approach taken here utilizes a hybrid neuro-fuzzy method where the neural network effectively chooses the optimum number of activation rules in order to reduce computational time for real-time applications. Initially, a classical fuzzy logic controller has been constructed for the path planning problem. The inference engine required 625 if-then rules for its implementation. Then the neural network is implemented to choose the optimum number of the activation rules based on the input crisp values. Simulation experiments were conducted to test the performance of the developed controller and the results proved that the approach to be practical for real time applications. The proposed neuro-fuzzy optimization controller is evaluated subjectively and objectively with other fuzzy approaches and also the processing time is taken in consideration.
Keywords : Fuzzy logic, mobile robot, neural network, rule-based optimization
Mammogram Image Size Reduction Using 16-8 bit Conversion Technique
Authors : Ayman A. AbuBaker, Rami S.Qahwaji, Musbah J. Aqel, and Mohmmad H. Saleh
Abstract : Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region
Keywords : Breast cancer, Image processing, Image reduction, Mammograms, Image enhancement
Mobile robot controller using novel hybrid system
Authors : Ayman AbuBaker , Yazeed Ghadi
Abstract : Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Keywords : Fuzzification Fuzzy logic Hybrid neuro-fuzzy Mobile robot Unstructured Environment
Comparison Study of Different Lossy Compression Techniques Applied on Digital Mammogram Images
Authors : Ayman AbuBaker, Mohammed Eshtay, Maryam AkhoZahia
Abstract : The huge growth of the usage of internet increases the need to transfer and save multimedia files. Mammogram images are part of these files that have large image size with high resolution. The compression of these images is used to reduce the size of the files without degrading the quality especially the suspicious regions in the mammogram images. Reduction of the size of these images gives more chance to store more images and minimize the cost of transmission in the case of exchanging information between radiologists. Many techniques exists in the literature to solve the loss of information in images. In this paper, two types of compression transformations are used which are Singular Value Decomposition (SVD) that transforms the image into series of Eigen vectors that depends on the dimensions of the image and Discrete Cosine Transform (DCT) that covert the image from spatial domain into frequency domain. In this paper, the Computer Aided Diagnosis (CAD) system is implemented to evaluate the microcalcification appearance in mammogram images after using the two transformation compressions. The performance of both transformations SVD and DCT is subjectively compared by a radiologist. As a result, the DCT algorithm can effectively reduce the size of the mammogram images by 65% with high quality microcalcification appearance regions.
Keywords : Mammogram Images; DCT Compression; SVD compression; Microcalcifications
Average Row Thresholding Method for Mammogram Segmentation
Authors : A.A. AbuBaker; R.S. Qahwaji; M.J. Aqel; M.H. Saleh
Abstract : Two novel threshold techniques are proposed for image segmentation which is a very critical task in any image processing. The two methods are based in scanning each image row by row and to find the proper threshold value. A modification of this method is developed to find the threshold value by average. The two methods are implemented on a mammogram and accordingly, a comparison between the two methods is carried out.
Keywords : image processing, image segmentation, threshold techniques, mammogram segmentation
A novel CAD system to automatically detect cancerous lung nodules using wavelet transform and SVM
Authors : Ayman AbuBaker , Yazeed Ghadi
Abstract : A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Keywords : Cancer detection Computed tomography DICOM Wavelet features Wavelet transform
Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
Authors : Ayman AbuBaker , Yazeed Yasin Ghadi , Nader Santarisi
Abstract : The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions..
Keywords : Enhancing mass lesions Fuzzy logic Mammograms Mass Lesions Neuro-fuzzy
Efficient Technique to Detect the Region of Interests in Mammogram Images
Authors : Moussa H. Abdallah, Ayman A. AbuBaker, Rami S. Qahwaji and Mohammed H. Saleh
Abstract : Problem Statement: Breast cancer is the second leading cause of cancer deaths in women today after lung cancer and is the most common cancer among women. The development of efficient technique to early detect the region of microcalcifications mammogram images is a must. Approach: The method proposed in this paper is to enhance the Computer Aided Diagnosis (CAD) performance. This automatic method can detect the region of interest in mammogram image accurately and efficiently using a modified standard deviation technique. The proposed method is divided to three steps: (a) reducing the mammogram image size, (b) segmentation the breast region, and, (c) detection the region of interest. Results: The application of the technique on 386 mammogram images from the MIAS and the USF databases showed that the method is so sensitive in detecting the microcalcifications in mammogram images with 98.9% detection of true positive. Conclusions: Hence the technique proposed showed major improvement in the detection of the micro calcification and the mass region.
Keywords : Mammogram, region of interest, modified standard deviation, microcalcifications
Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images
Authors : Ayman AbuBaker
Abstract : Computer aided diagnoses can assists radiologists in detecting microcalcification, crucial evidence in mammogram for the early diagnosis of breast cancer. A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique. As a first stage, the mammogram intensities are fuzzified using three linguistic labels. Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time applications. The parallel cascade neural networks are trained using data sets that randomly selected from the original fuzzy decision matrix. Finally, the value of the local mask centre is enhanced after defuzzification the input sets. This work is extensively evaluated using two different types of resources which are Mammographic Image Analysis Society database (MIAS) and University of South Florida (USF) database. As a result, it found to be sensitive in enhancing the microcalcifications regions in mammogram with very little number false positive regions.
Keywords : Mammograms; Microcalcifications; Enhancing MCs, Fuzzy logic, Neuro-fuzzy
Texture-Based Feature Extraction for the Microcalcification from Digital Mammogram Images
Authors : Ayman AbuBaker; Rami Qahwaji; Stan Ipson
Abstract : This paper describes our ongoing efforts to provide efficient and accurate classification of microcalcification clusters in mammogram images. In this paper, a study of the characteristics of true microcalcifications compared to falsely detected microcalcifications is carried out using first and second order statistical texture analysis techniques. These features are generated in order to reduce the false positive (FP) ratio for the mammogram images. The statistical method presented here can successfully reduce the ratio of false positives (FP) by 18% without affecting the ratio of true positives (TP) which is currently at 98%..
Keywords : Feature extraction, Image texture analysis, Image analysis, Biomedical imaging, Histograms, Statistical analysis, Brightness, Image segmentation, Neural networks, Shape measurement
An Adaptive CAD System to Detect Microcalcification in Compressed Mammogram Images
Authors : Ayman AbuBaker
Abstract : Microcalcifications (MC) in mammogram images are an early sign for breast cancer and their early detection is vital to improve its prognosis. Since MC appears as small dot in the mammogram image with size less than 1 mm and maybe easily overlooked by the radiologist, the Computer Aided Diagnosis (CAD) approach can assist the radiologist to improve their diagnostic accuracy. On the other hand, the mammogram images are a high resolution image with large image size which makes difficult the image transfer through the media. Therefore, in this paper, two image compressions techniques which are Discrete Cosine Transform (DCT) with entropy coding and Singular Value Decomposition (SVD) were investigated to reduce the mammogram image size. Then a novel adaptive CAD system is used to test the quality of the processed image based on true positive (TP) ratio and number of detected false positive (FP) regions in the mammogram image. The proposed adaptive CAD system used the visual appearance of MC in the mammogram to detect a potential MC regions. Then five texture features are implemented to reduce number of detected FP regions in the mammogram images. After implementing the adaptive CAD system on 100 mammogram images from USF and MIAS databases, it was found that the DCT can reduce the image size with a high quality since the ratio of TP is 87.6% with 11 FP/regions while in SVD the TP ratio is 79.1% with 26 FP/regions.
Keywords : Mammogram image; texture features; Discrete Cosine Transform (DCT); Singular Value Decomposition (SVD)
Autonomous system to control a mobile robot
Authors : Ayman Abu Baker , Yazeed Yasin Ghadi
Abstract : This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Keywords : Fuzzy logic Mobile robot Navigation system Unstructured environment