Comput Math Methods Med - Rotation covariant image processing for biomedical applications.

Tópicos

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{ framework(1458) process(801) describ(734) }
{ can(981) present(881) function(850) }
{ concept(1167) ontolog(924) domain(897) }
{ featur(1941) imag(1645) propos(1176) }
{ model(3480) simul(1196) paramet(876) }
{ assess(1506) score(1403) qualiti(1306) }
{ extract(1171) text(1153) clinic(932) }
{ process(1125) use(805) approach(778) }
{ imag(1057) registr(996) error(939) }
{ general(901) number(790) one(736) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
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{ method(2212) result(1239) propos(1039) }
{ data(1737) use(1416) pattern(1282) }
{ imag(2830) propos(1344) filter(1198) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ method(1557) propos(1049) approach(1037) }
{ research(1218) medic(880) student(794) }
{ signal(2180) analysi(812) frequenc(800) }
{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
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{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(984) reconstruct(947) comput(926) }
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{ perform(999) metric(946) measur(919) }
{ blood(1257) pressur(1144) flow(957) }
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{ state(1844) use(1261) util(961) }
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{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ time(1939) patient(1703) rate(768) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Resumo Limpo

advent novel biomed d imag acquisit techniqu effici reliabl analysi volumetr imag becom import amount data enorm demand autom process applic manifold rang imag enhanc imag reconstruct imag descript objectfeatur detect highlevel contextu featur extract scenario expect geometr transform alter output mathemat welldefin manner paper emphasi d translat rotat mani algorithm reli intens loword tensoriallik descript fulfil demand paper propos general mathemat framework base mathemat concept theori transfer mathemat physic harmon analysi domain imag analysi pattern recognit base two basic oper spheric tensor differenti spheric tensor multipl show design varieti d imag process method effici way framework alreadi appli sever biomed applic rang featur object detect task imag enhanc imag restor techniqu paper propos method appli varieti differ d data modal stem medic biolog scienc

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