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Medicine science fair project
Image Processing and Machine Learning for Early Diagnosis of Melanoma




Project Information
Title: A Novel Implementation of Image Processing and Machine Learning for Early Diagnosis of Melanoma
Subject: Medicine
Subcategory: Medical image computing
Grade level: High School - Grades 10-12
Academic Level: Advanced
Project Type: Engineering
Cost: Medium
Awards: Google Science Fair Finalist
Affiliation: Google Science Fair
Year: 2013
Concepts: Medical image computing, Artificial Neural Networks (ANN), Machine Learning, Statistical Analysis
Description: A multi-step system was created for early diagnosis of melanoma cancers. Image processing algorithms (edge detection and image segmentation) were used to extract the standard ABCD (Asymmetry, Border, Color, and Diameter) features of a skin mole. The extracted ABCD features were analyzed statistically to understand the impact of each characteristic. The features were then further tested in a machine learning algorithm known as Artificial Neural Networks for a comprehensive diagnosis. These combined steps provided about 80% accuracy and can successfully function as preliminary cancer diagnosis.
Link: https://www.googlesciencefair.com/en/projects/ahJzfnNjaWVuY
Short Background

Medical image computing

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, data science, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care.

The main goal of MIC is to extract clinically relevant information or knowledge from medical images. While closely related to the field of medical imaging, MIC focuses on the computational analysis of the images, not their acquisition. The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others.

Segmentation is the process of partitioning an image into different segments. In medical imaging, these segments often correspond to different tissue classes, organs, pathologies, or other biologically relevant structures. Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. Although there are many computer vision techniques for image segmentation, some have been adapted specifically for medical image computing. Below is a sampling of techniques within this field; the implementation relies on the expertise that clinicians can provide.

Image registration is a process that searches for the correct alignment of images. In the simplest case, two images are aligned. Typically, one image is treated as the target image and the other is treated as a source image; the source image is transformed to match the target image. The optimization procedure updates the transformation of the source image based on a similarity value that evaluates the current quality of the alignment. This iterative procedure is repeated until a (local) optimum is found. An example is the registration of CT and PET images to combine structural and metabolic information.

Visualization plays several key roles in Medical Image Computing. Methods from scientific visualization are used to understand and communicate about medical images, which are inherently spatial-temporal. Data visualization and data analysis are used on unstructured data forms, for example when evaluating statistical measures derived during algorithmic processing. Direct interaction with data, a key feature of the visualization process, is used to perform visual queries about data, annotate images, guide segmentation and registration processes, and control the visual representation of data (by controlling lighting rendering properties and viewing parameters). Visualization is used both for initial exploration and for conveying intermediate and final results of analyses.

Medical images can vary significantly across individuals due to people having organs of different shapes and sizes. Therefore, representing medical images to account for this variability is crucial. A popular approach to represent medical images is through the use of one or more atlases. Here, an atlas refers to a specific model for a population of images with parameters that are learned from a training dataset.

Shape Analysis is the field of Medical Image Computing that studies geometrical properties of structures obtained from different imaging modalities. Shape analysis recently become of increasing interest to the medical community due to its potential to precisely locate morphological changes between different populations of structures, i.e. healthy vs pathological, female vs male, young vs elderly. Shape Analysis includes two main steps: shape correspondence and statistical analysis.

See also:
https://en.wikipedia.org/wiki/Medical_image_computing

Source: Wikipedia (All text is available under the terms of the GNU Free Documentation License and Creative Commons Attribution-ShareAlike License.)

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