Segmentation in Echocardiographic Sequences Using Shape-Based Snake Model

Transcription

1 Segmentaton n chocarographc Sequences Usng Shape-Base Snake Moel Chen Sheng 1, Yang Xn 1, Yao Lpng 2, an Sun Kun 2 1 Insttuton of Image Processng an Pattern Recognton, Shangha Jaotong Unversty, Shangha, P.R. Chna chnshn@hotmal.com, yangxn@sjtu.eu.cn 2 Shangha Chlren s Mecal Center, Shangha Secon Mecal Unversty, Shangha, P.R. Chna Abstract. A novel metho for segmentaton of carac structures n temporal echocarographc sequences base on the snake moel s presente. The metho s motvate by the observaton that the structures of neghborng frames have consstent locatons an shapes that a n segmentaton. To cooperate wth the constranng nformaton prove by the neghborng frames, we combne the template matchng wth the conventonal snake moel. Furthermore, n orer to auto or sem-automatcally segment the sequent mages wthout manually rawng the ntal contours n each mage, generalze Hough transformaton (GHT) s use to roughly estmate the ntal contour by transformng the neghborng pror shape. As a result, the actve contour can easly etect the esrable bounares n ultrasoun mages. 1 Introucton nocaral bounary etecton n ultrasoun mages s a necessary step to obtan both qualtatve measurements (.e., the etecton of pathologcal eformaton) an quanttatve measurements (.e., area, volume an etc.). Unfortunately, ths s a ffcult task ue to the poor spatal an contrast resolutons, a hgh level of speckle nose an etc. To over come these problems, varous algorthms are propose to extract the bounares of the regon of nterest (ROI) n echocarographc mages [1]. These approaches can be manly categorze base on Markov ranom fel [2], artfcal neural network [3], mathematcal morphologcal [4] an eformable moel [5], etc. In these schemes, the eformable moel [6], whch s also known as the snake moel, s the most mportant an popular moel for nosy an low contrast mage segmentaton. In ths paper, the man reason for usng the snake moel s that t allows the ncorporaton of geometrc constrants. However, the conventonal eformable moels have some efcences for bounary etecton n ultrasoun mages. Frstly, the ntal contour generally has to be place qute close to the esrable bounary. Secon, when the snake moel s use to track the object n an mage sequence by usng the fnal contour from the prevous frame as the ntal contour n the current frame, the trackng works well only for small frame-to-frame splacement of ROI. Otherwse, the erve contour may be F. Rol an S. Vtulano (s.): ICIAP 25, LNCS 3617, pp , 25. Sprnger-Verlag Berln Heelberg 25

2 376 C. Sheng et al. easly trappe n a local mnmum forme by the nose. To remey ths problem, many technques were propose, for example, graent vector flow (GVF) [7], ual snake [8] an screte snake [9]. In ths paper, t s notce that the bounares of any two ajacent mages n a sequence are correlate to a certan egree. The result foun n one mage can be use as the shape template for the ajacent one. Thus, the only one rough shape template n a sequence nees to be gven manually at the frst step. For large frame-to-frame splacement of ROI, such as the mtral valve, GHT [1] s utlze to transform the shape template to an ntal contour n the ROI. It has been proven that GHT s able to etect any arbtrary shape unergong a geometrc transformaton n an mage. Moreover, t has shown to be robust an can even be successfully use to etect overlappng or sem-occlue objects n nosy mages. Our metho s base on the template matchng whch ncorporates the pror shape template from the ajacent frame nto the snake moel. Optmzng the eformaton energy between the shape template an the actve contour, the shape of the actve contour s constrane to be smlar to the template n global whle stll allowng slght eformaton locally. Uner ths energy crteron, the contour can escape from the local mnmum cause by the speckle, the tssue-relate textures. 2 Methos 2 Let Ω be a boune open subset of R. Let an C( s) = ( x() s, y() s )( s [,1] ) u : Ω R be a gven mage, be a parameterze contour wth s the parameter of length. The shape-base snake moel s to mnmze the followng energy: where nt ( C ( u C, C ) α ( C ) + β ( u, C ) + ( C, C ), t nt ext η C s the actve contour, ) ( u ), = (1) Ct s the shape template. s the nternal energy that controls the smoothness of the contour: ' '' ( C ) = C ( s) s C ()s s + (2) nt ext C s the external energy that attracts of the actve contour evolvng to the bounary of object. In ths paper, t s calculate from the texture nformaton nstea of the local graent n the ultrasoun mage. Furthermore, a blurrng Gaussan flter s apple for better result. However, the blurre texture feature probably loses some object bounary nformaton. Hence, the orgnal mage feature s also use to retan the bounary nformaton. Let T ( x, enote the texture mage after applyng the texture analyss to the orgnal mage u ( x,. The blurrng Gaussan flter s apple to the texture mage T ( x, to obtan the blurre texture mage T G ( x,. Now the external energy s efne as: ext ext con ( u C ) u ( C ( s) ) T ( C () s ) =, (3) G t

3 con Segmentaton n chocarographc Sequences Usng Shape-Base Snake Moel 377 ( C C ), s the energy to measure the smlarty between the actve contour an t the shape template. In ths paper, our metho has been nspre by the approach ue to Duncan [11], who propose a scheme for matchng two contours base on the mnmzaton of a quaratc fttng crteron, whch conssts of a curvature epenent benng energy term an a smoothness term. Duncan [11] ntrouces a local benng energy measure of the form: curvature ' ( kc ( s ) kc () s ) t = s where ( ' ' kc s ) s the curvature of the actve contour C at s as well as kc t (s). We also wsh the splacement vector fel to vary smoothly along the actve contour: smooth = s ' ( C ( s ) Ct () s ) s So the crteron s compose of the curvature constrant an the smooth constrant: elastc curvature 2 smooth (4) (5) = + λ (6) 3 Intalzaton of the Actve Contour The shape template must be approxmate as a vector contanng a sequental screte ponts n orer to solve by numercal metho, U = u, u,, u ] Â where ( u u ) ( x, { : x, y = 1,2, M } x, y, V = [ v1, v2,, vn. [ 1 2 n u =. The same metho s use for the actve contour, ] Before processng the bounary etecton by the snake, an ntal contour must be raw. The purpose of the ntalzaton s to place the ntal contour as close as possble to the bounary n ROI n orer to obtan a fast convergence n the bounary etecton. In ths paper, the GHT s apple to solve ths problem. Let us efne a geometrc transformaton of the shape template by: a A b U x t A x V = AU + t = + ( A A baca ) ca A U y t y a (8) where A an t correspon to a lnear transformaton an to a translaton vector respectvely. The potental locaton of the poston parameters t for the potental parameters A of the lnear transformaton can be expresse as t( U, V, A) = V AU. Ths metho traces an ntal contour n the parameter space, an after gatherng all evences for all ROI pxels, the maxmum of the accumulator array efnes the best values A an t whch correspon to the transformaton that maps the shape template to the echocarographc mage. The GHT can elver a relable estmaton of the ROI poston or a coarse ntal contour.

4 378 C. Sheng et al. 4 xperments an Results In ths secton, several examples are presente to llustrate the effcency of the shapebase snake moel for bounary etecton n echocarographc sequences. Sx sequent ultrasoun mages wth sze pxels were obtane from the Phlp 55 system, each coverng one complete carac cycle an contanng F = 16 frames. The algorthm has been mplemente usng an Intel Pentum IV 2.4Ghz wth 1 GB RAM, uner the Vsual C++ 6. envronment. To assess the performance of our segmentaton metho, we compare automatcally etecte caro structure bounares wth the manual outlnes. In ths paper, four sets of manual outlnes are gven for each of the sequences. Two sets of parameters are employe: the mean, the stanar evaton (SD), an the maxmum of the mnmal stances from the erve bounary ponts to the manual outlne. They are use to measure the fference between the erve contour an the outlne n one frame of a sequence. Let C an C enote the erve contour an the manual outlne, respectvely. For each m p C, fn p Cm j where For all (, ) so that p = mn p p C p j j m arg, p p means the uclean stance between the two pxels. p, computer the uclean stance. p Compute the mean, the SD an the maxmum of { }. p C We nee another set of parameters to evaluate the segmentaton results for the whole sequence, so the mean an the SD of the mean absolute stance (MAD) are efne as follow: The MAD between two contours A an B s efne as: n m D( A, B) = ( a ) + ( ), B b, A. 2 n = 1 m = 1 D D S, where S s all the MADs nee to be Compute the mean, the SD of { } calculate for a sequence. 4.1 Process of Segmentaton Fg.1 shows the segmentaton process for a mtral valve sequence. The ntal contour of the ( k +1) th mage obtane rectly from the fnal contour of the k th mage s shown n Fg.1 (c). Fg.1 (b) presents the ntal contour, whch has been transforme by GHT. In the Fg.1 (), we can see that the segmentaton result rather conces wth the contour manually efne by an nepenent octor n Fg.1 (e) when usng GHT to locate the ntal contour. On the other han, we can see that the shape-base snake moel treats well when there s a gap n the tp of the leaflet uner the shape constrant.

5 Segmentaton n chocarographc Sequences Usng Shape-Base Snake Moel 379 Fg. 1. xample of segmentaton for mtral valve; (a) the kth mage wth fnal contour; (b) the (k+1)th mage wth ntal contour from the kth mage usng GHT; (c) the (k+1)th mage wth ntal contour rect from the kth mage; () the (k+1)th mage wth segmentaton result usng ntal contour n (b); (e) manual outlne for the (k+1)th mage Table 1. The mean, the SD an the maxmum of the mnmal stances for Fg.2 Mnmal stances Mean [pxels] SD [pxels] Max [pxels] Usng GHT Wthout usng GHT Fg. 2. xample of segmentaton for left ventrcle; (a) the kth mage wth fnal contour; (b) the (k+1)th mage wth ntal contour from the kth mage usng GHT; (c) the (k+1)th mage wth ntal contour rect from the kth mage; () the (k+1)th mage wth segmentaton result usng ntal contour n (b); (e) the (k+1)th mage wth segmentaton result usng ntal contour n (c); (f) manual outlne for the (k+1)th mage It may be reasonable to say that the segmentaton result closely follows the esre bounary. Nevertheless, the algorthm fals when usng the ntal contour n Fg.1 (c) although the same energy weghtng factors ( α = 1., β = 1., η =. 5 ) are gven. GHT s not neee n all stuatons such as the small frame-to-frame splacement of the structure. Fg.3 (e) shows the segmentaton result for the left ventrcle wth the ntal contour rect from the prevous mage s entcal to that usng GHT to locate the ntal contour (Fg.3 ()). The evaluate parameters of the segmentaton results are shown n Table 1. Both the mean an the SD of the mnmal stances are near to each other.

6 38 C. Sheng et al. 4.2 Segmentaton of nocaral Bounares n Sequences In four sequences, the algorthm was use to segment the enocaral bounares. Some frames from the frst sequence are shown n Fg.3. Table 2 shows the mean an the SD of the MADs for the whole sequence between the algorthm-generate contours an the four sets of manual outlnes an between fferent manual outlnes. These experments show that the segmentaton results compare well to the manual outlnes for the enocaral bounares. Fg. 3. Characterstc frames showng the segmentaton results of the left ventrcle Table 2. Results of the comparson between the algorthm-generate contours an the manual outlnes Seq1 Seq2 Seq3 Seq4 Mean of MADs between snake an outlnes [pxels] SD of MADs between snake an outlnes [pxels] Mean of MADs between fferent manual outlnes [pxels] SD of MADs between fferent manual outlnes [pxels] Segmentaton of Mtral Valve Sequences The algorthm performance was evaluate on two sequences of long axs vew mages of the mtral valve. Characterstc frames from the frst sequence are shown n Fg.4. As one coul expect, the fferences of ROI between any two ajacent frames are larger, but the algorthm performance s stll comparable to the manual segmentatons. Fg. 4. Characterstc frames showng the segmentaton results of the mtral valve Table 3. Results of the comparson between the algorthm-generate contours an the manual outlnes for sequences contanng mages of the mtral valve Seq1 Seq2 Mean of MADs between snake an outlnes [pxels] SD of MADs between snake an outlnes [pxels] Mean of MADs between fferent manual outlnes [pxels] SD of MADs between fferent manual outlnes [pxels].69.52

7 Segmentaton n chocarographc Sequences Usng Shape-Base Snake Moel 381 Table 3 shows the evaluate results for mtral valve sequences. In ths Table, we can see that the mean an the SD are larger than those n Table 2. It may be ascrbe to at least two factors. The frst one s that the manual outlnes may vary wth experts. The secon factor s that the contours n the mtral valve mages are open. The startng ponts an the enng ponts efne by the experts may vary largely. As a result, the MAD between the open contours may be larger than that between close contours. 4.4 Determnaton of Weghtng Factors α β, η In our experments, the weghtng factors, are set n Table 4. The moton of the mtral valve s very rregular, frame-to-frame splacements are several tmes larger than the leaflet thckness. At those phases, the leaflet rotates, translates an eforms at the same tme. As a result, the fference of shape between two ajacent mages may be large. Table 4. Values of parameters use n the algorthm α β η enocaral sequences Mtral valve sequences GHT algorthms are known to be computatonally expensve (about 6 mn for a sequence n our experments) an they are not neee n all stuatons. So, n our metho, the GHT was separate from the snake eformaton process. A user can ntervene when or where GHT to be use. However, these algorthms o not nee user s supervson urng the segmentaton process. The user s nteracton was neee n just one frame for a sequence. 5 Conclusons In ths paper, an nnovatve moel has been propose for echocarographc mage segmentaton, namely, the shape-base snake moel. The propose shape-base moel ams to ncorporate the template matchng an the GHT wth the snake moel. The moel can resst the speckle nose, tssue-relate textures an artefacts, an gue the actve contour eform to the esrable bounary. The prncpal ea of ths moel s to use GHT to estmate the ntal contour, an then usng the elastc eformaton energy between the shape template an the actve contour to gue the contour eform from the local mnmum. Our metho oes not nee to raw a precse shape template, but rather a rough contour regarless of ts poston, scalng an rotaton only once n a sequence. Acknowlegments Ths work was partally supporte by Natonal Scence Research Program of Chna (No. 24BA71482) an Shangha Scence an Technology Development Founaton ( ).

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