Biomedical Image Processing

Number: 141220 
Event Type: Lecture and Computer Exercise 
Module Representative: Prof. Dr.-Ing. Georg Schmitz 
LecturerDr.-Ing. Ste­fa­nie Dencks 
Language: German 
SWS: 4 
CP: 5 
   Start: Friday, 04/08/2022 
   Lecture: Friday, 08:15 AM - 09.​45 AM, ID 04/445 
   Exercise: Tuesday, 10:15 - 11.​45 AM, CIP-Pool 1 
   Date by arrangement with the lecturer. 
   Type of Exam:oral
   Exam Registration:Flex­Now
   Duration:30 min



After suc­cess­ful com­ple­ti­on of the mo­du­le, stu­dents have know­ledge of mul­ti­di­men­sio­nal di­gi­tal si­gnal pro­ces­sing. They know and un­der­stand the ac­qui­si­ti­on of mul­ti­di­men­sio­nal image data of the most im­portant dia­gnostic ima­ging me­thods, can model these and de­ri­ve con­se­quen­ces for their pro­ces­sing. Stu­dents can clas­si­fy the dif­fe­rent steps of image pro­ces­sing into ab­stract task ca­te­go­ries (e.g. fil­te­ring, seg­men­ta­ti­on, clas­si­fi­ca­ti­on), know selec­ted pro­ce­du­res in de­tail, and can ex­plain and apply them. The stu­dents are able to ana­ly­se a given image pro­ces­sing task and to de­ve­lop and al­go­rith­mi­cal­ly im­ple­ment a sui­ta­ble so­lu­ti­on. The me­thods are taught using me­di­cal image data as an ex­amp­le, but stu­dents can also trans­fer the me­thods to other ap­p­li­ca­ti­on areas. Ex­er­ci­ses in small groups on com­pu­ters enable the stu­dents to apply the ac­qui­red know­ledge in a small team, and to ex­plain and di­s­cuss their so­lu­ti­ons.


Basic prin­ci­ples and spe­ci­fic me­thods of image pro­ces­sing are in­tro­du­ced, which are par­ti­cu­lar­ly ap­p­lied to me­di­cal image data. Howe­ver, se­ver­al of these me­thods are also ap­p­lied in other ap­p­li­ca­ti­on areas, e.g. in in­dus­tri­al image pro­ces­sing.

In the first sec­tion, both the re­cep­ti­on by the human vi­su­al sys­tem is out­lined, and the stu­dents are fa­mi­li­a­ri­zed with the de­fi­ni­ti­ons and basic prin­ci­ples of image pro­ces­sing as well (dis­cre­tiza­t­i­on, sam­pling theo­rem, glo­bal pa­ra­me­ters of ima­ges). The se­cond sec­tion im­parts know­ledge of the most im­portant ope­ra­ti­ons in the image do­main (his­to­gram mo­du­la­ti­on, fil­te­ring, mor­pho­lo­gi­cal ope­ra­ti­ons, geo­me­tric ope­ra­ti­ons, dis­tan­ce trans­form, ...). The third sec­tion com­pri­ses me­thods of in­for­ma­ti­on extrac­tion (seg­men­ta­ti­on al­go­rith­ms, tex­tu­re ana­ly­sis, de­scrip­ti­on of shape). The fourth sec­tion fo­cu­ses on clas­si­fi­ca­ti­on and va­rious me­thods of ma­chi­ne le­arning (e.g. sup­port vec­tor ma­chi­nes, deep le­arning). The topic of the fifth sec­tion is image re­stau­ra­ti­on. Ad­di­tio­nal­ly, an over­view of image re­gis­tra­ti­on and 3D-vi­sua­liza­t­i­on is given.




Know­ledge of sys­tem theo­ry, Fou­rier trans­form, and si­gnal pro­ces­sing equi­va­lent to the level of Ba­che­lor in Elec­tri­cal En­gi­nee­ring and In­for­ma­ti­on Tech­no­lo­gy are a pre­re­qui­si­te. Basic pro­gramming skills in Mat­lab are ad­van­ta­ge­ous.


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This course is organized via Moodle. The necessary information will be given in the first lecture. 

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