2015 Summer Project Week:Dicom parsing with DCMJS

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  Projects List

Key Investigators

• Nicolas Rannou
• Michael Onken
• Steve Pieper
• Yves Martelli

Project Description

Objective

• Define target DICOM types
• Define a good workflow to parse targets with DCMJS.
• Define required JS models to support the workflow.
• Evaluate DicomParser from cornerstone team.

Approach, Plan

• Talk with DICOM/DCMTK experts to get some feedback on best strategy.
• Write a demo to showcase js models and dicom parsing workflows.

Progress

• Evaluation of current solutions
  • yves:
    • pros: fast
    • cons: single frame and need to parse dicom header by hand in JS later on
  • dicomparser: same
  • dcmjs:
    • pro: relies on dcmtk, (almost) no need to manually parse dicom header by hand
    • cons: slower (150ms for dcmdump vs 30ms for other solutions)
  • note: compilation of dcmjs with -o3 or -of drasticly improves performance of dcmjs
• Proposed workflow
  • https://docs.google.com/drawings/d/1BX0NBIDPrrM-JGk9b1awL_w57f9rMvdTJSeVjT9bM5U
  • note: workflow relies on new dcmtk functionnalities. It will have to be evaluated in term of performance.
• Take dicom segmentations/models into account
• Testing database

Benchmark

Benchmark of DICOM parser libraries using benchmark.js. Units are in operations/seconds.

	dcmjs-nico		dicomParser		dwv
	ff	chrome	ff	chrome	ff	chrome
gdcm-CT-MONO2-8-abdo	320	112	16115	9974	4551	3640
gdcm-CR-MONO1-10-chest	249	83.13	8412	9004	3603	2804
osirix-toutatix-100	170	48.65	3245	3843	1411	995
osirix-goudurix	189	53.97	6346	3995	1885	1363
dicompyler-ct.0	187	60.86	8414	5156	2612	1930
gdcm-US-RGB-8-epicard	148	54.04	10418	10217	31.42	256
gdcm-US-RGB-8-esopecho	501	110	10309	10198	4642	3340
Average	252	75	9037	7484	2676	2047
				3.4
					3.7
		0.03
			0.01