Medical Image Processing

Extracting High-Level Intuitive Features (HLIF) For Classifying Skin Lesions Using Standard Camera Images

by Robert Amelard

9th Canadian Conference on Computer and Robot Vision, Accepted

High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are... more High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are presented. These features can be used to help dermatologists objectively diagnose lesions as cancerous (melanoma) or benign with intuitive rationale. Existing work defines large sets of low-level statistical features for analysing skin lesions. The proposed HLIFs are designed such that smaller sets of HLIFs can capture more deterministic information than large sets of low-level features. Analytical reasoning is given for each feature to show how it aptly describes asymmetry. Promising experimental results show that classification using the proposed HLIF set, although only one-tenth the size of the existing state-of-the-art low-level feature set, labels the data with better or comparable success. The best classification is obtained by combining the low-level feature set with the HLIF set.

Nonrigid free-form registration using landmark-based statistical deformation models

by Stefan Pszczolkowski Parraguez

In this paper, we propose an image registration algorithm named statistically-based FFD registration (SFFD). This... more In this paper, we propose an image registration algorithm named statistically-based FFD registration (SFFD). This registration method is a modification of a well-known free-form deformations (FFD) approach. Our framework dramatically reduces the number of parameters to optimise and only needs to perform a single-resolution optimisation to account for coarse and fine local displacements, in contrast to the multi-resolution strategy employed by the FFD-based registration. The proposed registration uses statistical deformation models (SDMs) as a priori knowledge to guide the alignment of a new subject to a common reference template. These SDMs account for the anatomical mean and variability across a population of subjects. We also propose that available anatomical landmark information can be encoded within the proposed SDM framework to enforce the alignment of certain anatomical structures. We present results in terms of fiducial localisation error, which illustrate the ability of the SDMs to encode landmark position information. We also show that our statistical registration algorithm can provide registration results comparable to the standard FFD-based approach at a much lower computational cost.

Vascular centerline extraction in 3D MR angiograms for phase contrast MRI blood flow measurement

by Piotr Orlowski

The accuracy of 2D phase contrast (PC) magnetic resonance angiography (MRA) depends on the alignment between the... more The accuracy of 2D phase contrast (PC) magnetic resonance angiography (MRA) depends on the alignment between the vessels and the imaging plane. PC MRA imaging of blood flow is challenging when the flow in several vessels is to be evaluated with one acquisition. For this purpose, semi-automatic determination of the plane most perpendicular to several vessels is proposed based on centerlines extracted from 3D MRA. Arterial centerlines are extracted from 3D MRA based on iterative estimation-prediction, multi-scale analysis of image moments, and a second-order shape model. The optimal plane is determined by minimizing misalignment between its normal vector and the centerlines’ tangent vectors. The method was evaluated on a phantom and on 35 patients, by seeking the optimal plane for cerebral blood flow quantification simultaneously in internal carotids and vertebral arteries. In the phantom, difference of orientation and of height between known and calculated planes was 1.2° and 2.5 mm, respectively. In the patients, all but one centerline were correctly extracted and the misalignment of the plane was within 12° per artery. Semi-automatic centerline extraction simplifies and automates determination of the plane orthogonal to one vessel, thereby permitting automatic simultaneous minimization of the misalignment with several vessels in PC MRA.

Efficient computation of Hessian-based enhancement filters for tubular structures in 3D images

by Piotr Orlowski

This work presents guidelines for a computationally efficient implementation of multiscale image filters based on... more This work presents guidelines for a computationally efficient implementation of multiscale image filters based on eigenanalysis of the Hessian matrix, for the enhancement of tubular structures. Our focus is the application to 3D medical images of blood vessels. The method uses matrix trace, determinant and sign to discard voxels unlikely to belong to vessels, prior to the calculation of the Hessian eigenvalues. As example of time savings, we provide results obtained in four computed tomography datasets (300 × 300 × 300 voxels) containing coronary and pulmonary arteries. The test based on the Hessian trace avoided the computation of the eigenvalues in half of the voxels on average, while the test combining the Hessian determinant and sign eliminated up to 10% additional voxels. The actual time savings depend on the algorithm used to compute the eigenvalues for the remaining voxels. With a very fast algorithm using a closed-form solution, the computational time was reduced from 20.5 to 12.5 seconds per scale, but the time gained thanks to the more complex of the two tests was negligible. However, this fast algorithm is prone to numerical instabilities. Accurate computation of the eigenvalues requires the use of iterative or hybrid algorithms. In this case, both tests produce time savings and the computational time can be reduced by several minutes per scale.

An Image Processing Technique for the Translation of ASL Finger-Spelling to Digital Audio or Text. Paper Presented at the Instructional Technology and Education of the …

by Chance Glenn

Content-aware display adaptation and interactive editing for stereoscopic images

by Chia-Kai Liang

Graph Based Image Segmentation Method for Identification of Cancer in Prostate MRI Image

by Jca Ksrce

Volume 4 Issue 4 - October - December 2011
Published in Journal of Computer Applications,
KSR College of Engineering, http://www.jcaksrce.org

N Gopinath
Keywords :Magnetic Resonance Imaging, Prostate Cancer, Anisotropic Diffusion, Pair Wise Region Comparison Predicate

Prostate cancer is one of the leading causes of cancer related death for men in the United States. In recent years,... more Prostate cancer is one of the leading causes of cancer related death for men in the United States. In recent years, multispectral Magnetic Resonance Imaging (MRI) has emerged as an alternative to Ultrasound (US) image modality for clear identification of cancer in Breast, Prostate and Liver etc,. In order to analyze a disease, Physicians consider MR imaging modality is the most efficient one for identification of tumors present in various organs. Therefore, analysis on MR imaging is required for efficient disease diagnosis. The proposed Graph based segmentation technique is a part of image analysis. In this  system, segmentation of MR image is carried out with three sequences of steps; Preprocessing using Anisotropic Filtering, Graph Construction using Minimum spanning tree based algorithm, and Segmentation using Pair wise Region Comparison Predicate and Region Mergence methods [1, 2]. This method is developed to improve the automatic detection of tumors in the given image than the manual segmentation. The output of the Graph based method can be compared with the Fuzzy C Means (FCM) algorithm to analyze the variations in the segmented image.

EARMARKING RETINAL CHANGES IN A SEQUENCE OF DIGITAL COLOR FUNDUS PHOTOGRAPHS

by João Filipe Ferreira

A Region-Based Edge Detection Technique for Noisy Images

by Mohammad Bagher Akbari Haghighat

The 3rd IEEE International Conference on Application of Information and Communication Technologies (AICT), pp. 1-5, Oct. 2009, Baku, Azerbaijan.

Edge detection is a basic and important issue in computer vision and image processing. The traditional methods of... more Edge detection is a basic and important issue in computer vision and image processing. The traditional methods of image edge detection are sensitive to noise and often cannot locate the edge exactly. We propose a new method of edge detection which can eliminate the fake edges caused by the noise disturbance, and ensure the veracity of edge orientation to have a proper result for noisy images. A problem introduced as "cross effect" occurs in the derivative of Gaussian filter method, which will be eliminated in this paper. As will be shown, our method is robust in confrontation with noise.

Multi-Focus Image Fusion for Visual Sensor Networks in DCT Domain

by Mohammad Bagher Akbari Haghighat

Computers and Electrical Engineering, Elsevier, vol. 37, no. 5, pp. 789-797, September 2011.

The objective of image fusion is to combine relevant information from multiple images into a single image. The... more The objective of image fusion is to combine relevant information from multiple images into a single image. The discrete cosine transform (DCT) based methods of image fusion are more efficient and time-saving in real-time systems using DCT based standards of still image or video. Existing DCT based methods are suffering from some undesirable side effects like blurring or blocking artifacts which reduce the quality of the output image. Furthermore, some of these methods are rather complex and this contradicts the concept of the simplicity of DCT based algorithms. In this paper, an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. Due to simplicity of our proposed method, it can be easily used in real-time applications. The experimental results verify the efficiency improvement of our method both in output quality and complexity reduction in comparison with several recent proposed techniques.

Keywords: Multi-focus image fusion, Visual sensor networks, DCT domain, JPEG, Consistency verification.

A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features

by Mohammad Bagher Akbari Haghighat

Computers and Electrical Engineering, Elsevier, vol. 37, no. 5, pp. 744-756, September 2011.

The widespread usage of image fusion causes an increase in the importance of assessing the performance of different... more The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria.

Keywords: image fusion, image fusion metric, mutual information, joint distribution, image feature.

Procesamiento de Imágenes basado en un Sistema de Creación y Muerte de Partículas

by Omar Sosa Tzec

Thesis - M.Sc. in Computing and Industrial Mathematics - Center for Research in Mathematics - Mexico

Filtrado no lineal de imágenes basado en un sistema de creación y muerte de partículas. Filtrado no lineal de imágenes basado en un sistema de creación y muerte de partículas.

Mammographic image restoration using maximum entropy deconvolution

by Adrian Jannetta

Co-authored with J C Jackson, C J Kotre, I P Birch, K J Robson, R Padgett
Published in Physics in Medicine and Biology (2004)

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological... more An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.

Characterization and scanning electron microscopic investigation of crosslinked freeze dried gelatin matrices for study of drug diffusivity and release kinetics

by Dr. Goutam Thakur

Goutam Thakur, Analava Mitra, Amit Basak, Debdoot Sheet, Micron 43:311–320(2012)

Drug delivery is a promising technique to enhance the therapeutic efficacy of the drug. However, properties of carrier... more Drug delivery is a promising technique to enhance the therapeutic efficacy of the drug. However, properties of carrier materials require intense improvement for effective transport of drug molecules. In the current study, attempts have been made to develop freeze dried gelatin matrices cross linked with genipin at various temperatures (5ºC, 15ºC and 25ºC) prior to freeze-drying (-80ºC). The freeze dried matrices thus obtained at the said temperatures are characterized for crosslinking density, compression strength, swelling behaviors. The matrix crosslinked at 25ºC showed highest Flory Rehner crosslinking density (467±46) (p<0.05), highest compressive strength (12.36±0.12) (p<0.05) and lowest equilibrium water content. In this context, scanning electron microscopy (SEM) was performed to study the surface morphology (size and shape of pores) of the crosslinked matrices. These images were further processed for quantitative analysis of morphological features viz., areas, radius, ferret diameter, length of major and minor axis and eccentricity using MATLAB toolboxes. These quantitative analyses correlate transport and the release kinetics of model anti-inflammatory drug (indomethacin) from crosslinked matrices in vitro to tune as a controllable delivery system. The diffusional exponent (n) for all constructs ranging from 0.61-0.69 (p<0.05) (0.45<n<0.89) indicated non-Fickian release kinetics

A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears

by Minh-Tam (Reynard) Le

Visual Pattern Mining in Histology Image Collections Using Bag of Features

by Angel Cruz

Feature-space clustering for fMRI meta-analysis

by Matthew Liptrot

Co-author. Published in Human Brain Mapping (2001)

Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible... more Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods

" Lyngby"-a Modeler's Matlab Toolbox for Spatio-Temporal Analysis of Functional Neuroimages

by Matthew Liptrot

MITK-based segmentation of co-registered MRI for subject-related regional anaesthesia simulation

by Sebastian Ullrich

Teich C, Liao W, Ullrich S, Kuhlen T, Ntouba A, Rossaint R, Ullisch M & Deserno TM: “MITK-based segmentation of co-registered MRI for subject-related regional anaesthesia simulation”, In Proceedings SPIE Medical Imaging 2008. San Diego CA, USA. 2008, February. pp. 69182M-69182M-10. SPIE Press.

With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop a... more With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop a virtual reality (VR)-based simulation, a patient model is needed containing several tissues, which have to be extracted from individual magnet resonance imaging (MRI) volume datasets. Due to the given modality and the different characteristics of the single tissues, an adequate segmentation can only be achieved by using a combination of segmentation algorithms. In this paper, we present a framework for creating an individual model from MRI scans of the patient. Our work splits in two parts. At first, an easy-to-use and extensible tool for handling the segmentation task on arbitrary datasets is provided. The key idea is to let the user create a segmentation for the given subject by running different processing steps in a purposive order and store them in a segmentation script for reuse on new datasets. For data handling and visualization, we utilize the Medical Imaging Interaction Toolkit (MITK), which is based on the Visualization Toolkit (VTK) and the Insight Segmentation and Registration Toolkit (ITK). The second part is to find suitable segmentation algorithms and respectively parameters for differentiating the tissues required by the RA simulation. For this purpose, a fuzzy c-means clustering algorithm combined with mathematical morphology operators and a geometric active contour-based approach is chosen. The segmentation process itself aims at operating with minimal user interaction, and the gained model fits the requirements of the simulation. First results are shown for both, male and female MRI of the pelvis.

MULTISENSORS AC BIOSUSCEPTOME- TRY FOR RECORDING SIMULTANEOUSLY THE ACTIVE CONTRACTION OF ENTIRE GASTROINTESTINAL TRACT IN RATS

by Paulo Fonseca

Marcelo R. Agostinho, Madileine F. Americo, Yuri K Sinzato, Murilo Stelzer, Jose Ricardo A. Miranda,Rozemeire G. Marques, Paulo R. Fonseca