Medical Image Analysis and Pattern Recognition Research Papers (original) (raw)

Selection of a good feature extraction method is the most important factor in achieving the higher recognition rate in face recognition. This paper presents the analysis of two moment based feature extraction methods namely Zernike moments (ZMs) and Complex Zernike moments (CZMs) in application to face image recognition. We have intensively analyzed these methods in terms of their reconstruction ability and invariance to rotation, scale and size. Almost all existing methods use only magnitude component of the moments as invariant features in recognition task. Recently it is proposed that the phase component of moments also captures useful information for image representation. In this paper, we have analyzed the performance of both magnitude and phase coefficients of ZMs and call it CZMs. These methods are tested separately on suitable databases. The databases used are UMIST pose database for rotation variation, JAFFE expression database for size and scale variations, and popular ORL and FERET databases for comparison of recognition results. It can be concluded from the experimental results that the performance of CZMs is not only better than ZMs but also it is the descriptor that gives best recognition rate amongst the descriptors well known for face recognition.

- by Joonki Paik
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- Pattern Recognition, Image Analysis, Vibration, Kalman Filter

Embedding a set of objects of arbitrary kind into a linear space by choosing an appro-priate two-argument function possessing properties of inner product (kernel function) is a convenient approach to solving most glowing problems of modern informatics such as that of finding empirical regularities in sets of signals and symbolic sequences of dif-ferent length. However, constructing kernel functions is no easy problem. In this work, we propose a sufficiently universal probabilistic principle of kernel function construc-tion on sets of signals and symbol sequences of different length, which is based on in-terpretation of every object as effect of random transformation of another object from the same set.

- by Valentina Sulimova and +1
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- Engineering, Medical Image Analysis and Pattern Recognition, Mathematical Sciences

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

- by Titas De
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- Bioengineering, Artificial Intelligence, Computer Vision, Image Processing
- by Colin Higgins
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- Arabic, Pattern Recognition, Character Recognition, Arabic Language
- by Andrea Bottino and +1
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- Image Processing, Image Analysis, Medical Image Analysis and Pattern Recognition, Attractiveness
- by xiaoyi feng
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- Engineering, Algorithms, Artificial Intelligence, Human Computer Interaction

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

- by Vania V Estrela
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- Applied Mathematics, Computer Graphics, Artificial Intelligence, Computer Vision
- by Ali El-Zaart
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- Engineering, Image Processing, Data Analysis, Image segmentation

Vehicle plate recognition is an effective image processing technique used to identify vehicles' plate numbers. There are several applications for this technique which expand through many fields and interest groups. Vehicle plate recognition may be used as a marketing tool, for purposes of traffic and border control, for law enforcement, and travel. Many methods have been proposed to facilitate this technique. This study proposes an edgedetection method to enable a Plate Recognition System through practical situations, such as various environmental or meteorological conditions. Image processing tools are used to scan the plate area, resize it, and convert it toward a gray scale prior to filtering the image in order to remove small objects. The obtained objects are identified such that the numbers object is recognized. The details of the obtained image are controlled through the standard deviation of the Gaussian filter (sigma).

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of

In recent years, Active contours have been widely studied and applied in medical image analysis. Active contours combine underlying information with high-level prior knowledge to achieve automatic segmentation for complex objects. Their applications include edge detection, segmentation of objects, shape modelling and object boundary tracking. This paper presents the development process of active contour models and describes the classical parametric active contour models, geometric active contour models, and new hybrid active contour models based on curve evolution and energy minimization techniques. It also discusses challenges and applications of active contour models in medical image segmentation.

Worldwide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80%, 10-20%, 5% or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed. INDEX TERMS MRI, prostate cancer, deep learning, automatic algorithms, prostate gland.

In the diagnosis of malignant melanoma, a skin cancer, the degree of irregularity along the skin lesion border is an important diagnostic factor. This paper presents a new measure of border irregularity based on conditional entropy. The measure was tested on 98 skin lesions of which 16 were malignant melanoma. The ROC analysis showed that the measure is 70% sensitive and 84% specific in discriminating the malignant and benign lesions. These results compare favourably with other measures and indicate that conditional entropy captures some distinguishing features in the boundary of malignant lesions.

- by Sule Anjomshoae and +1
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- Engineering, Medical Image Analysis and Pattern Recognition, Mathematical Sciences

In this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of ...

- by Hasan Demirel
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- Computer Science, Neural Networks, Affective Computing, Facial expression

Applied behavioral analysis (ABA) is an effective form of therapy for children with autism spectrum disorder (ASD), but it faces criticism for being un-generalizable, too time intensive, and too dependent on specialists to deliver treatment. Earlier age at onset of therapy is one of the strongest predictors of later success, but waitlists to begin therapies can be as long as 18 months. To combat complications associated with the clinical setting and expedite access to therapy, we have begun development of Autism Glass, a machine-learning-assisted software system that runs on Google Glass and an Android Smartphone; it is designed for use in the child’s natural environment during social interactions.This is an exploratory- and co-designed-based study to see how children with ASD respond to our device and examine preliminary data on its effectiveness.

- by Titas De and +1
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- Cognitive Behavioral Therapy, Artificial Intelligence, Human Computer Interaction, Computer Vision

Ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body. The main disadvantage of medical ultrasonography is the poor quality of images, which are affected by multiplicative speckle noise. In this paper, we present a novel method for despeckling medical ultrasound images. The primary goal of speckle reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of the wavelet transform and apply multi-resolution analysis to localize an image into different frequency components or useful subbands and then effectively reduce the speckle in the subbands according to the local statistics within the bands. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless. The objective of the paper is to investigate the proper selection of wavelet filters and thresholding schemes which yields optimal visual enhancement of ultrasound images, in particular. We employ the wavelet shrinkage denoising techniques with different wavelet bases and decomposition levels on the individual subbands to achieve the best acceptable speckle reduction while maintaining the fidelity of the image and also examine the effects of different thresholding techniques as well as shrinkage rules for denoising ultrasound images. The proposed method consists of the log transformed original ultrasound image being subjected to wavelet transform, which is then denoised by a thresholding technique using a shrinkage rule. Experimental results show that the subband decomposition of ultrasound images, using Bior6.8 and level 3 with soft thresholding based on Bayes shrinkage rule, performs better than other techniques. The performance is measured in terms of Variance, Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak SNR (PSNR) and Correlation Coefficient (CC). The results of wavelet shrinkage techniques are compared with common speckle filters. We observe that the proposed method achieves better visual enhancement of ultrasound images which would lead to more accurate image analysis by the medical experts.

A chest x-ray screening system for pulmonary pathologies such as tuberculosis (TB) is of paramount importance due to the increasing mortality rate of patients with undiagnosed TB, especially in densely-populated developing countries. As a first step toward developing such screening systems, this paper presents a novel computer vision module that automatically segments the lungs from posteroanterior digital chest x-ray images. The segmentation task is non-trivial, due to poor image contrast and occlusion of the lung region by ribs, clavicle, heart, and by non-TB abnormalities associated with pulmonary diseases. In the proposed procedure, we first compute a lung shape model by employing a level set based technique for registration up to a homography. Next, we use this computed mean lung shape to initialize the level set that is based on a best fit measure obtained in a heuristically estimated search space for the projective transform parameters. Once the level set is initialized, a suite of customized lower level image features and higher level shape features up to a homography evolve the level set function at a lower resolution in order to achieve a coarse segmentation of the lungs. Finally, a fine segmentation step is performed by adding additional shape variation constraints and evolving the level set in a higher resolution. We processed the standard Japanese Society of Radiological Technology (JSRT) dataset, comprised of 247 images, using this scheme. The promising results (92% accuracy) demonstrate the viability and efficacy of the proposed approach. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

- by Sridharan Kamalakannan
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- Radiology, Computer Vision, Image Processing, Calculus of Variations
- by Frank Deinzer
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- Engineering, X-ray imaging, X Rays, Medical Image Analysis and Pattern Recognition

In this paper, we present a real-time ellipse detector in gray scale images with a new multiple stage architecture based on a 3-accumulator version of the Fast Hough Transform with a previous Canny edge extraction. The system can be applied to detect different elliptical objects and is robust to incomplete ellipses, cluttered backgrounds and illumination changes. It achieves 12 frames per second on a PC Pentium 4, 2.80 GHz. Experimental results, focusing on faces, with both static and video images are showed. The presented ellipse detector can be used as a preprocessing module in a face tracking or recognition application.

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

- by Felipe Lumbreras and +1
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- Medical Image Analysis and Pattern Recognition, Missing Data, Photometric Stereo, Three Dimensional
- by Levy Santana and +1
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- Engineering, Decision Making, Medical Image Analysis and Pattern Recognition, Mathematical Sciences
- by Norbert Haala and +1
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- Engineering, Pattern Recognition, Medical Image Analysis and Pattern Recognition, Urban Modeling

Abstract In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern... more

- by Ovidio Salvetti
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- Engineering, Computer Science, Data Mining, Pattern Recognition

- by Joaquin Enrique Chirinos Salas
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- Image Processing, Modeling, Image Analysis, Stereopsis

Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.