Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.

Intelligent visual surveillance — A survey

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

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Image-Based Cardiac Diagnosis With Machine Learning: A Review.

Backbone anatomical structure detection and labeling is a necessary step for various analysis tasks of the vertebral column. Appearance, shape and geometry measurements are necessary for abnormality detection locally at each disc and vertebrae (such as herniation) as well as globally for the whole spine (such as spinal scoliosis). We propose a two-level probabilistic model for the localization of discs from clinical magnetic resonance imaging (MRI) data that captures both pixel- and object-level features. Using a Gibbs distribution, we model appearance and spatial information at the pixel level, and at the object level, we model the spatial distribution of the discs and the relative distances between them. We use generalized expectation-maximization for optimization, which achieves efficient convergence of disc labels. Our two-level model allows the assumption of conditional independence at the pixel-level to enhance efficiency while maintaining robustness. We use a dataset that contains 105 MRI clinical normal and abnormal cases for the lumbar area. We thoroughly test our model and achieve encouraging results on normal and abnormal cases.

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Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model

Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions

Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.

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Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features

In this paper we present a genetic-algorithm-based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme is applied to discriminate myocardial heart disease from echocardiographic images and to detect and classify clustered microcalcifications from mammograms. Unlike the conventional types of membership functions such as trapezoid, triangle, S curve, and singleton used in fuzzy reasoning, Gaussian-distributed fuzzy membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features obtained from reference images. Subsequently the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used for disease discrimination. The results of our experiments are very promising. We achieve an average accuracy of 96% for myocardial heart disease and accuracy of 88.5% at 100% sensitivity level for microcalcification on mammograms. The results demonstrated that our proposed genetic-algorithm-based fuzzy-logic approach is an effective method for computer-aided diagnosis in disease classification.

Medical image classification using genetic-algorithm based fuzzy-logic approach

The paper presents a wavelet-based method for medical image enhancement. A transfer function is derived for transferring wavelet coefficients at various multiresolution levels with different weighting values. Applications of the method to digital radiographic images of chest X-rays are shown. Moreover, in order to validate the effectiveness of the proposed method, we compare the results obtained using the wavelet analysis method to that using the conventional fast Fourier transform (FFT) method. The results show that the proposed method is superior to the FFT method in terms of image enhancement.

A method of medical image enhancement using wavelet analysis

In this paper we describe a method for assisting radiological technologists in their routine work to automatically determine the imaging plane in lumbar MRI. The method is first to recognize the spinal cord and the intervertebral disk (ID) from the lumbar vertebra 3-plane localizer image, and then the imaging plane is automatically determined according to the recognition results. To determine the imaging plane, the spinal cord and the ID are automatically recognized from the lumbar vertebra 3-plane localizer image with a series of image processing techniques. The proposed method consists of three major steps. First, after removing the air and fat regions from the 3-plane localizer image by use of histogram analysis, the rachis region is specified with Sobel edge detection filter. Second, the spinal cord and the ID were respectively extracted from the specified rachis region making use of global thresholding and the line detection filter. Finally, the imaging plane is determined by finding the straight line between the spinal cord and the ID with the Hough transform. Image data of 10 healthy volunteers were used for investigation. To validate the usefulness of our proposed method, manual determination of the imaging plane was also conducted by five experienced radiological technologists. Our experimental results showed that the concordance rate between the manual setting and automatic determination reached to 90%. Moreover, a remarkable reduction in execution time for imaging-plane determination was also achieved.

http://www.clg.niigata-u.ac.jp/~lee/paper/spie2006_6144_139.pdf

Automatic determination of the imaging plane in lumbar MRI

An integrated fuzzy-GA-based cad system for disease discrimination

Rolf Maximilian Sievert’s father, Max Sievert (1849–1913), was the eldest son of Heinrich Theodor Sievert, who served as the chairman of the town council in Zittau, East Germany (near the border of Czechoslovakia). After completing the compulsory education of the time, Max often visited Russia and Northern European countries for business purposes while working for an agency that specialized in the handling of machines. He felt that acting as an interface between Germany and Sweden through trading would surely lead him to success and established the Max Sievert Company in Stockholm in 1881, specializing in the wholesale trade of machines. The company not only imported German machines and tools, but expanded by exporting products manufactured in Sweden. The company grew substantially by producing telephone cables in Sweden and became the Max Sievert Manufacturing Co. Ltd. Max obtained Swedish citizenship in 1885 and married Sofia Carolina Pancheen in 1894. Their daughter, Lisa, was born in 1895, and son Rolf Sievert was born on May 6, 1896. The children grew up comfortably. Max died in 1913 when Sievert was 17 years old. Sievert inherited an enormous fortune from his father, enough so that he could live a comfortable life on the interest alone. 2 Education

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Masaru Sekiya

Papers

Medical image classification using genetic-algorithm based fuzzy-logic approach

A method of medical image enhancement using wavelet analysis

Automatic determination of the imaging plane in lumbar MRI

An integrated fuzzy-GA-based cad system for disease discrimination

Rolf Maximilian Sievert (1896-1966): father of radiation protection.