Difference between revisions of "2016 Winter Project Week/Projects/BatchImageAnalysis"

Revision as of 06:43, 8 January 2016

3D SIFT Lung Features

Objective	Approach and Plan	Progress and Next Steps
Run feature detection code over a collection of medical images pulled from PACS Investigate a collection of ADC maps of neonates (diffusion MR) Patients labeled with age and health status (normal, mildly abnormal, severely abnormal) Use 3D SIFT code to see if health status can be detected in images (if time) try text analysis of radiology reports	Use deidentified cohort of neonate images collected from MGH Install data and software on AWS, try StarCluster Explore visualization options (if time) integrate image features with analysis of radiology report text	Algorithm feature extraction (20 seconds per image) Feature matching O(log N) indexing (< 1 second per image) 3D SIFT-Rank code (Windows, Linux, Max) and read me http://www.matthewtoews.com/fba/featExtract1.5.zip Result Baseline HIE classification rate: 73%, leave-one-out moderate vs normal. Data 231 subjects, Apparent Diffusion Coefficient (ADC) images.

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 * feature extraction (20 seconds per image)
-* feature matching O(log N) indexing (< 1 second per image)
+* Feature matching O(log N) indexing (< 1 second per image)
 * 3D SIFT-Rank code (Windows, Linux, Max)  and read me
 http://www.matthewtoews.com/fba/featExtract1.5.zip
-Leave-one-out classification rate for HIE: moderate vs normal: 73%
+Result
+Baseline HIE classification rate: 73%, leave-one-out moderate vs normal.
 Data