Developing and Using Computational Frameworks to Conduct Numerical Analysis and Calculate Temperature Profiles and to Classify Breast Abnormalities

Author(s):  
Kamila Fernanda F. da C. Queiroz ◽  
Marcus Costa de Araújo ◽  
Nadja Accioly Espíndola ◽  
Ladjane C. Santos ◽  
Francisco G. S. Santos ◽  
...  

In this chapter, computational tools that have been designed to analyze and classify infrared (IR) images will be presented. The function of such tools is to interconnect in a user-friendly way the algorithms that are used to map temperatures and to classify some breast pathologies. One of these performs texture mapping using IR breast images to relate temperatures measured to the points over the substitute tridimensional geometry mesh. Another computer-aided diagnosis (CAD) tool was adapted so that it could be used to evaluate individual patients. This methodology will be used when the computational framework approach for classification is described. Finally, graphical interfaces and their functionalities will be presented and explained. Some case studies will be presented in order to verify whether or not the computational classification framework is effective.

2017 ◽  
pp. 614-654
Author(s):  
Rishu Gupta ◽  
Irraivan Elamvazuthi ◽  
John George

Non-invasive diagnostic imaging methods for diagnosis of pathological conditions is increasingly gaining popularity resulting from speedy and effective recovery during follow up in several clinical trials. The accuracy of the diagnosis depends on the experience and knowledge of physicians conducting the trial. In such scenario, the need for quantitative measures for details such as shape and size of tissue can assist physicians for better intuitive understanding of tissue and its pathologies. Computer aided diagnosis (CAD) tool incorporating methods for segmentation, texture analysis and area computation can increase the accuracy of diagnosis by providing quantitative analysis of the image. This chapter briefly describes issues and challenges for building the CAD tool followed by brief description about the methods involved. The methods are validation are also discussed briefly. To summarize the work, brief discussion about a new software or CAD tool for diagnosis of pathologies supraspinatus tendon with the help of ultrasound images is provided. The new software has an intuitive user interface which is easy, quick and suitable for medical work.


Author(s):  
Rishu Gupta ◽  
Irraivan Elamvazuthi ◽  
John George

Non-invasive diagnostic imaging methods for diagnosis of pathological conditions is increasingly gaining popularity resulting from speedy and effective recovery during follow up in several clinical trials. The accuracy of the diagnosis depends on the experience and knowledge of physicians conducting the trial. In such scenario, the need for quantitative measures for details such as shape and size of tissue can assist physicians for better intuitive understanding of tissue and its pathologies. Computer aided diagnosis (CAD) tool incorporating methods for segmentation, texture analysis and area computation can increase the accuracy of diagnosis by providing quantitative analysis of the image. This chapter briefly describes issues and challenges for building the CAD tool followed by brief description about the methods involved. The methods are validation are also discussed briefly. To summarize the work, brief discussion about a new software or CAD tool for diagnosis of pathologies supraspinatus tendon with the help of ultrasound images is provided. The new software has an intuitive user interface which is easy, quick and suitable for medical work.


1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.


2019 ◽  
Author(s):  
S Kashin ◽  
R Kuvaev ◽  
E Kraynova ◽  
H Edelsbrunner ◽  
O Dunaeva ◽  
...  

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