scholarly journals Chromosome Analysis by Image Processing in a Computerized Environment. Clinical Applications

1992 ◽  
Vol 33 (SUPPLEMENT) ◽  
pp. 171-188 ◽  
Author(s):  
P. MALET ◽  
M. BENKHALIFA ◽  
B. PERISSEL ◽  
A. GENEIX ◽  
B. LE CORVAISIER
Keyword(s):  
Image Processing ◽  
Chromosome Analysis ◽  
Clinical Applications ◽  
Computerized Environment

scholarly journals CT-based ventilation imaging in radiation oncology

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20180035 ◽  
Author(s):  
Yevgeniy Vinogradskiy
Keyword(s):  
Image Processing ◽  
Clinical Trials ◽  
Radiation Therapy ◽  
Lung Function ◽  
Dose Response ◽  
Radiation Oncology ◽  
Planning Process ◽  
Response Assessment ◽  
Clinical Applications ◽  
Breath Hold

A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose–response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose–response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.

Image processing techniques for 3-D chromosome analysis

1990 ◽  
Vol 158 (2) ◽  
pp. 235-248 ◽  
Author(s):  
A. B. Houtsmuller ◽  
J. L. Oud ◽  
H. T. M. Voort ◽  
M. W. Baarslag ◽  
J. J. Krol ◽  
...  
Keyword(s):  
Image Processing ◽  
Chromosome Analysis ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Clinical applications of chromosome analysis, from fine needle aspiration biopsies, of posterior uveal melanomas

Eye ◽  
1998 ◽  
Vol 12 (2) ◽  
pp. 203-207 ◽  
Author(s):  
Karen Sisley ◽  
Carmel Nichols ◽  
M Andrew Parsons ◽  
Robin Farr ◽  
Robert C Rees ◽  
...  
Keyword(s):  
Fine Needle Aspiration ◽  
Chromosome Analysis ◽  
Needle Aspiration ◽  
Clinical Applications ◽  
Fine Needle ◽  
Uveal Melanomas

scholarly journals Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

2020 ◽  
Author(s):  
Samuel Ortega ◽  
Martin Halicek ◽  
Himar Fabelo ◽  
Eduardo Quevedo ◽  
Baowei Fei ◽  
...  
Keyword(s):  
Image Processing ◽  
Information Extraction ◽  
Hyperspectral Imaging ◽  
Light Propagation ◽  
Clinical Applications ◽  
Electromagnetic Spectrum ◽  
Learning Approaches ◽  
Extraction Techniques ◽  
Present Information ◽  
Detection And Diagnosis

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

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