Efficient Method for Image Indexing in Medical Application

2011 ◽  
pp. 257-264
Author(s):  
Richard Chbeir
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Image Retrieval ◽  
Efficient Method ◽  
Medical Application ◽  
Image Indexing ◽  
Exact Matching ◽  
Standard Data ◽  
Image Processing Algorithms ◽  
Processing Algorithms

In last two decades, image retrieval has seen a growth of interests in several domains. As a result, a lot of work has been done in order to integrate it in the standard data processing environments (Rui, Huang, & Chang, 1999; Smeulders, Gevers, & Kersten, 1998; Yoshitaka & Ichikawa, 1999). To retrieve images, different methods have been proposed in the literature (Chang & Jungert, 1997; Guttman, 1984; Lin, Jagadish, & Faloutsos, 1994). These methods can be grouped into two major approaches: metadata-based and content-based approaches. The metadata-based approach uses alphanumeric attributes and traditional techniques to describe the context and/or the content of the image such as title, author name, date, and so on. The content-based approach uses image processing algorithms to extract low-level features of images such as colors, textures, and shapes. Image retrieval using these features is done by methods of similarity and hence is a non-exact matching.

Efficient Method for Image Indexing in Medical Application

2011 ◽  
pp. 705-714
Author(s):  
Richard Chbeir
Keyword(s):  
Image Retrieval ◽  
Medical Application ◽  
Image Indexing ◽  
Exact Matching ◽  
Standard Data ◽  
Subsequent Section ◽  
Novel Method ◽  
Image Processing Algorithms ◽  
Future Work ◽  
Powerful Mechanism

In last two decades, image retrieval has seen a growth of interests in several domains. As a result, a lot of work has been done in order to integrate it in the standard data processing environments (Rui, Huang, & Chang, 1999; Smeulders, Gevers, & Kersten, 1998; Yoshitaka & Ichikawa, 1999). To retrieve images, different methods have been proposed in the literature (Chang & Jungert, 1997; Guttman, 1984; Lin, Jagadish, & Faloutsos, 1994). These methods can be grouped into two major approaches: metadata-based and content-based approaches. The metadata-based approach uses alphanumeric attributes and traditional techniques to describe the context and/or the content of the image such as title, author name, date, and so on. The content-based approach uses image processing algorithms to extract low-level features of images such as colors, textures, and shapes. Image retrieval using these features is done by methods of similarity and hence is a non-exact matching. In this article, we address the spatial and evolutionary issues of images. We propose a novel method that considers different types of relations. This method allows providing a highly expressive and powerful mechanism for indexing images. The rest of this article is organized as follows: the next section is devoted to detail the related work. In the following section, we define our method of computing the different relations and we show how image indexing can be done. The subsequent section demonstrates how our method can adequately index medical images. Finally, we conclude and give future work orientations.

scholarly journals Remote Sensing for Maritime Prompt Monitoring

2019 ◽  
Vol 7 (7) ◽  
pp. 202 ◽  
Author(s):  
Marco Reggiannini ◽  
Marco Righi ◽  
Marco Tampucci ◽  
Angelica Lo Duca ◽  
Clara Bacciu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Data Processing ◽  
Automatic Processing ◽  
Heterogeneous Data ◽  
Radar Images ◽  
Maritime Traffic ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Optical Imagery

The main purpose of this paper is to describe a software platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade pipeline of several image processing algorithms that input Radar or Optical imagery captured by satellite-borne sensors and try to identify vessel targets in the scene and provide quantitative descriptors about their shape and motion. This platform is innovative since it integrates in its architecture heterogeneous data and data processing solutions with the goal of identifying navigating vessels in a unique and completely automatic processing streamline. More in detail, the processing chain consists of: (i) the detection of target vessels in an input map; (ii) the estimation of each vessel’s most descriptive geometrical and scatterometric (for radar images) features; (iii) the estimation of the kinematics of each vessel; (iv) the prediction of each vessel’s forthcoming route; and (v) the visualization of the results in a dedicated webGIS interface. The resulting platform represents a novel tool to counteract unauthorized fishing and tackle irregular migration and the related smuggling activities.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Elastic Graph-based Semi-supervised Embedding with Adaptive Loss Regression

2020 ◽  
Keyword(s):  
Image Processing ◽  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

Sign in / Sign up

Export Citation Format

Share Document