MRI Abdominal Organ Tissue Identification using Statistical Distance in Color Space

Transcription

1 MRI Abdomial Orga Tissue Idetificatio usig Statistical Distace i Color Space Ared Castelei, Terrace Weede, Xiupig Tao 3, H. Keith Brow, Yog Wei. Departmet of Computer Sciece, Uiversity of North Georgia, Dahloega, GA 30597, USA. Departmet of Cliical Aatomy, Philadelphia College of Osteopathic Medicie-Georgia, Suwaee, GA 3004, USA 3. Departmet of Chemistry, Wisto Salem State Uiversity, Wisto Salem, NC 70, USA Submitted to: IPCV'3: The 03 Iteratioal Coferece o Image Processig, Computer Visio ad Patter Recogitio Abstract Magetic resoace imagig (MRI) is a powerful medical imagig techique to provide detailed images of soft abdome orga tissues. A automatic orga tissue idetificatio algorithm is useful for physicias to perform iitial readig ad iterpret MRI images. The algorithm preseted i the paper uses the distace i color space betwee ceters of orga tissues to idetify abdomial orgas i MRI images. Experimetal results show the algorithm is effective i the RGB, LAB ad AB color spaces. Key words: orga idetificatio, MRI, image processig.. Itroductio Magetic resoace imagig (MRI) is a powerful medical imagig techique to provide detailed images of soft abdome orga tissues. Maual orga tissue idetificatio by medical dosimetrist is time cosumig. To become a dosimetrist requires special traiig. Hece, developig a automatic orga tissue idetificatio algorithm is useful for physicias to perform iitial readig ad iterpret MRI images. Segmetatio ad idetificatio of abdomial orgas, such as kidey, liver, splee, pacreas ad stomach, from CT ad MRI images, has bee attractig a fair amout of research. Lee et al. [] use a eural etwork that takes advatage of shape aalysis, image cotextual costrait, ad betwee-slice relatioship to extract discoected regios from CT images. Fuimoto et al. [] extract ad recogize abdomial orgas from CT images usig 3D mathematical morphology. To overcome the problem of itesity ihomogeeity i MRI images, the parametric method for bias field estimatio is used by Li et al. [3] The fuzzy versio of k-meas clusterig (fuzzy c-meas, FCM) is widely adopted for vector quatizatio ad data compressio [4][5]. This work uses color fusio MRI methodology to extract color images from logitudial relaxatio time T ad trasverse relaxatio time T images. A previously established stadardized acquisitio ad image processig protocol is used to produce color MRI images of a variety of abdomial tissues of huma subects. We assume that pixels for each tissue obect i a MRI image have similar property, whilst pixels i differet orga tissues are differet i color space. Therefore, idetifyig a orga tissue i a MRI image becomes the problem of fidig the closest set of pixels of a kow tissue i the color space.

2 I this work, abdomial MRI images are first segmeted usig fuzzy c-meas clusterig i the l a b color space [5]. A physicia ca choose a regio of iterest of a ukow abdomial orga i the segmeted image. Statistical distaces betwee the ceters of kow abdomial orga tissues ad the regio of iterest of a ukow orga are used to idetify which orga it belogs to. T-test is performed to further verify the results. Experimets show that the algorithm yields very satisfactory results. Results obtaied from data i various color spaces are explored as well. The remaider of the paper is as follows. Sectio describes the statistical distace-based orga idetificatio algorithm. Sectio 3 discusses the experimetal data preparatio ad results. Sectio 4 cocludes the paper with a outlie for future work.. Statistical Distace-based Orga Idetificatio The differet orgas or tissues have differet combiatios of biophysical parameters that are mapped i MR images. Whe each of the parameter maps are assiged color masks ad the fused ito a sigle full color image, tissues with differet biophysical parameters are displayed as differet i color. Tissues with very similar biophysical parameters appear as very similar i color. We assume that color is a costat property for each tissue obect i a MRI image. Therefore idetifyig orga i a abdomial MRI image becomes the problem of fidig the shortest distace betwee the ceter of a kow abdomial orga tissues ad the regio of iterest of the ukow orga. The algorithm is desiged to idetify differet orgas from a MRI image based o the color ceter of that image. The color ceter of a regio of iterest is defied as follows: C i i (.) where C represets the ceter of color chael (for example red, gree or blue i RGB space) ad is the umber of pixels i the regio. The distace for each pixel from the ceter of color chael C is calculated as follows d i m ( i C ) (.) where d i is the Euclidia distace from ceter of the color space for pixel i ad m is the umber of chaels i the color space. The mea of the distaces is the calculated as follows d d i i (.3)

3 where is the umber of pixels i the regio of iterest. Ad the the stadard deviatio is calculated as show here ( d i d) (.4) i This process is repeated for all the sets up images represetig differet orgas. After a ukow orga i a image, represeted as u, is selected the probable orga idetity is determied through the followig process. The ceter of the color space C u ad the umber of pixels i the image are foud as they were i the predefied data. The it iterates through the followig process for each kow orga k. It starts by calculatig the Euclidia distace betwee the ceter for the ukow image regio ad the ceter for kow orga k. d uk m ( C C ) (.5) u k m represets the umber of color chaels i the image. duk represets the distace betwee the ceters of ukow image regio u ad kow orga k. This distace is the coverted ito a t-score usig the followig equatio t uk duk (.6) / k where t uk represets the t-score for image regio u compared to kow orga k. Basig the idetity of the image regio solely upo its distace from the orgas color ceter ca cause problems. I the graph below [6] otice that the ceter of the image, represeted by the red lie, is closer to orga B yet accordig to the frequecy diagram the prospect of the orga adherig to orga A is more likely. Fig. Ceters of Orga Image ad Color Distributios Usig t-distributio takes this problem ito accout by defiig the distace i terms of how may stadard deviatios it is away from the orga.

4 3. Experimetal Results Images used i experimet are extracted from logitudial relaxatio time T ad trasverse relaxatio time T MRI images. The ColorMRI tm methodology geerates full color images from the plurality of gray toe images acquired by magetic resoace imagig. The gray toe images are essetially mappigs of biophysical/uclear magetic resoace parameters such as logitudial relaxatio timet, trasverse relaxatio time T, proto desity (PD), magetic susceptibility, gadoliium cotrast media ehacemet, etc. Assigmet of color masks to each biophysical parameter image ad subsequet fusio of the color masked images results i a full color image i which the uique color of each pixel i the RGB color space represets the combiatio of uique biophysical parameters of the tissue represeted by that pixel. Followig is a example of a abdomial color MRI of labeled regios of iterest. Liver, pacreas ad kidey are labeled. Hepatic tissue (liver) is located o the far left side of the image. Real tissue is foud adacet to the liver. Pacreatic tissue is located toward the ceter of the image. Pacreas Liver Kidey Fig. 3. A Abdomial Color MRI of Labeled Regio of Iterest I the experimets, MRI images are separated ito two groups. The traiig data set cotais 80% of the whole set. The rest 0% are for the testig data set. From both the traiig ad testig data set, images are first segmeted usig fuzz c-meas clusterig described i [5]. The regios of iterest (ROI) of various abdomial orgas are maually selected. The ceter of each ROI is calculated usig equatio (.). The mea value of distace betwee the ROI of a ukow orga ad every kow orga is obtaied usig equatio (.3). We use equatio (.5) to calculate the t-score for ROI of a ukow orgra compared to a kow orga. A low t-score idicates fewer stadard deviatios from the ceter therefore the orga with the lowest t-score is determied to be the idetity of the image. Table 3. shows the ceters ad stadard deviatios of orgas i the traiig data set i the RBG, LAB ad AB color space respectively.

5 Name of Orga Ceters i RGB Ceters i LAB Ceters i AB Stadard Deviatio i RGB Stadard Deviatio i LAB Stadard Deviatio i AB Kidey [ 4,87,4 ] [ 04,43,7 ] [ 43, 7 ] Liver [ 97,53,49 ] [ 7, 47, 40 ] [ 47, 40 ] Muscle [ 78,3,4 ] [ 49, 5, 3 ] [ 5, 3 ] Pacreas [ 07, 66, 59 ] [ 83, 46, 40 ] [ 46, 40 ] Splee [ 4, 7, 94 ] [ 9, 50, ] [ 50, ] Stomach [ 43,74,3 ] [ 78,4,04 ] [ 4, 04 ] Table 3. Ceters ad Stadard Deviatios of Kow Orgas of Traiig Data i RGB, LAB ad AB Color Space Accuracy (%) Kidey Liver Muscle Pacreas Splee Stomach I RGB I LAB I AB Usig t-score i RGB Usig t-score i LAB Usig t-score i AB Table 3. Orga/Tissue Idetificatio Results Results of orga idetificatio are listed i Table 3.. It is demostrated that the statistical distace-based idetificatio algorithm yields very satisfactory results i both the RGB ad LAB color spaces. The behavior of the algorithm i the AB space deserves a discussio. I the RGB color space, differeces amog colors perceived by the huma eye as beig of the same etity are ot mirrored by similar distaces betwee the poits represetig those colors i the color spaces. The problem is reduced i the CIE LAB color space [7]. I the CIE LAB space, L represets the brightess, i.e. itesity. From the results show i Table 3., it is oted that color itesities play a importat role i distiguishig tissues. Orga Name Distace i RGB Space Distace i LAB Space Distace i AB Space Kidey Liver Muscle Pacreas Splee Stomach Table 3.3 Distaces of a ROI of Pacreas from Other Orgas I our experimets, ROIs of pacreas are mis-idetified as liver tissue i the AB space. Table 3.3 shows the distaces of it from kow orgas. This is due to the histological differeces betwee

6 the two orgas eve though both are gladular orgas. Hepatic tissue has a more extesive blood supply tha the pacreas, allowig it to store iro, sythesize proteis, ad house may mitochodria that cotai cytochrome c. The presece of ferriti ad cytochrome c has a effect o the imagig of the liver i that it lowers the sigal of the waves that are recorded by the MRI scaer. This is evideced by the brightess values of liver ad pacreas listed i Table 3.. Liver ad pacreas tissue brightess values i the LAB space are 7 ad 83 respectively. I the mea time, their AB compoets are similar, [ 47,40] ad [ [ 46,40] for liver ad pacreas respectively. 4. Coclusios I this paper, we use the statistical distace-based algorithm to idetify abdomial orga i MRI images. Experimetal results show that distace betwee ceters of regios of iterest of orga tissues ca effectively distiguish tissues. Developig a automatic orga tissue idetificatio algorithm is useful for physicias to perform iitial readig ad iterpret MRI images. I the future, the algorithm will be further tested o more MRI data, ad will be exteded to idetify other huma tissues. Refereces [] Chie-Cheg Lee, ad Pau-Choo Chug, Recogizig abdomial orgas i CT images usig cotextual eural etworks ad fuzzy rules, i Proc. d Au. It. Cof. of the IEEE Egieerig i Medicie ad Biology Society, Chicago, IL, July 3-8, 000. [] Hideyuki Fuimoto, Lixu Gu, ad Toyohisa Kaeko, Recogitio of abdomial orgas usig 3D mathematical morphology, Tras. Ist. Electro. If. Commu. Eg. D-II, o. 5, pp , May 00. [3] Chumig Li, CheyagXu, Adam W. Aderso, ad Joh C. Gore, "MRI Tissue Classificatio ad Bias Field Estimatio Based o Coheret Local Itesity Clusterig: A Uified Eergy Miimizatio Framework", J.L. Price, D.L. Pham, ad K.J. Myers (Eds.): IPMI 009, LNCS 5636, pp , Spriger, 009. [4] Ala Wee-Chug Liew, ad Hog Ya, A adaptive spatial fuzzy clusterig algorithm for 3- D MR image segmetatio. IEEE Tras. Med. Imag. (9), (003). [5] Yog Wei, H. Keith Brow, Xiupig Tao, Samuel Moore, Dogshe Che, "Brai MRI Image Segmetatio usig Fuzzy C-Meas Clusterig", Proc. IPCV 00, Las Vegas, Nevada, USA, July -5, 00. [6] Oswald Sotag, Quality i the aalytical phase, Biochemia Medica 0():47-53, 00. [7] L. Lucchese ad S.K. Mitra, Color image segmetatio: A state-of-the-art survey, Image Process. Vis. Patter Recog. Proc., Idia Nat. Sci. Acad. (INSA-A), v67 i, 00, pp. 07-.