CHAPTER-3 SEGMENTATION OF BLOOD VESSELS FROM DIGITAL FUNDUS IMAGES

Transcription

1 CHAPTER-3 SEGMENTATION OF BLOOD VESSELS FROM DIGITAL FUNDUS IMAGES Ocular fundus image assessmen has been exensively used by ophhalmologiss for diagnosing vascular and non vascular pahology. Examining he reinal blood vessel nework may reveal areriosclerosis, diabees, hyperension, cardiovascular disease and sroke [12]. Furhermore, he segmenaion of he vessel nework is he mos suiable represenaion for he reinal image regisraion since vascular ree does no change excep in a few diseases and includes adequae informaion for he idenificaion of some anchor poins. In addiion, vessel ree can also be used as a land mark feaure for image-guided laser reamen of choroidal neovascularizaion. Therefore reliable mehods for segmenaion of blood vessels in fundus images are needed. The mehods used for blood vessel segmenaion discussed in Chaper-2 can work well o segmen he major pars of he blood vessels. However, he major challenges confroning he vessel segmenaion mehods which are shown in Fig. 3.1are: Segmenaion of he hinner blood vessels as he image conras is normally low around hin blood vessels; The presence of pahologies as hey may be mis-enhanced and mis-deeced as vessels.

2 Fig.3.1. Challenges in Exracion of Reinal Vasculaure having Severe Pahology from STARE Daabase. Arrows drawn on he image in black dashed lines show lesions and he boundary of he opic disc., Arrows drawn in whie lines highligh narrow blood vessels in low conras regions. In order o solve hese problems, Hisogram Mached Local Relaive Enropy (HMLRE) mehod is developed o segmen blood vessels in fundus images. For efficien deecion of vasculaure, high conras beween vessel nework and he fundus background is desired while here mus be low conras beween he fundus background and reinal pahologies [93]. In red channel of a RGB colour image, he gray levels are spread over a wider range compared o green channel. Therefore he conras beween brigh pahologies and fundus background is less in red channel. Thus in HMLRE mehod, red channel s inensiy informaion is used in pre-processing of colour fundus images. The hisogram of green channel is modified by employing he hisogram of red channel (of he same fundus image) o

3 obain a new image in preprocessing. The conras of vasculaure agains he background of he preprocessed image is improved by using mached filer. The local relaive enropy hresholding wih hisogram compression and ranslaion is employed o discriminae blood vessel segmens from he background in he mached filer response. The misclassified pixels are hen removed by using label filering. The performance of he proposed HMLRE mehod is evaluaed on he publicly available STARE [20] and DRIVE [94] daabases using Receiver Operaing Characerisic (ROC) curve analysis. Experimenal evaluaion of he HMLRE mehod demonsraes subsanial performance over oher blood vessel segmenaion algorihms recenly repored in he lieraure. This chaper is srucured as follows. In Secion 3.1, he HMLRE mehod is presened. Implemenaion deails of HMLRE mehod are described in Secion 3.2. Experimenal resuls are presened and compared wih exising mehods in Secion 3.3. Conclusions are given in Secion PROPOSED ALGORITHM The proposed HMLRE mehod consiss of four seps as shown in fig Firsly, he hisogram of green channel is modified by employing he hisogram of red channel (of he same fundus image) o obain a new preprocessed image. Secondly, o enhance he blood vessels in he hisogram mached image a 2D mached filer kernel is applied. Then, local relaive enropy hresholding is used o

4 differeniae blood vessel segmens from he background in he mached filer response image. Finally, label filering is exploied o remove he misclassified pixels. Hisogram maching is applied o make use of he inensiy informaion of red and green channels. The inensiy informaion of red channel is used for wo reasons: To enhance he visual appearance of fundus images in cases of varying illuminaion. To improve he performance of blood vessel segmenaion. Therefore he hisogram of he green channel is modified by using he hisogram of red channel (of he same fundus image) o aain a new image. To improve he conras of vasculaure agains he fundus background, mached filer is employed. In order o properly segmen he enhanced vessel segmens in he mached filer response images, an effecive hresholding scheme is necessary. An efficien local relaive enropy based hresholding mehod ha akes ino accoun he spaial disribuion of gray levels is used, because some mached filer response images have complicaed relaionships or overlap beween foreground and background. Paricularly, hresholding based on local relaive enropy [95] is implemened which can well mainain he srucure deails of an image. Conneced componen labeling is applied o idenify individual objecs in each relaive local enropy hresholded image. Conneced componen labeling is an image

5 Inpus: Red and Green channels of he reinal image Green channel Yes C r 0..3 No Hisogram maching of green channel wih red channel Mached filer Thresholding based on Local Relaive Enropy Label filering Deecion resul Fig.3.2. Flow Char of he Proposed HMLRE Mehod

6 analysis mehod ha scans an image pixel by pixel. I groups all he pixels ino componens depending on pixel conneciviy. The label filering aemps o separae he individual objecs by make use of he eigh conneced neighborhood informaion and label propagaion IMPLEMENTATION DETAILS The implemenaion deails of he proposed HMLRE mehod are explained in his secion PREPROCESSING USING HISTOGRAM MATCHING For exracing blood vessel nework, he unsupervised mehods generally use he green channel of he colour fundus image as i has he bes vessel/background conras. The red channel of he colour fundus image has he following advanages; The red channel is brigher. In red channel he gray level values are spread over a wider range. This resuls in less conras beween pahologies and reinal background ha can be observed in Fig Hence in he proposed HMLRE mehod, inensiies of boh he red and green channels of he same fundus image are uilized. Hisogram maching is applied o modify he hisogram of green channel by employing he hisogram of red channel (of he same fundus image) o ge a new image ha have he advanages of boh he channels. For reinal images having very high brighness, hisogram maching of green and red channel images

7 (a) (c) (e) (g) (b) (d) (f) (h) Fig.3.3. (a) Healhy Colour Reinal Image (b) Colour Reinal Image wih Severe Pahology (c) & (d) Red Channel Images (e) & (f) Green Channel Images (g) & (h) Blue Channel Images. reduces he conras beween vasculaure and is background. This leads o an image wih low conras han he conras in he green channel. In hese cases, green channel images are preferred over he hisogram mached images. To overcome his problem a condiion is applied wheher o use hisogram maching or no. The condiion o use green channel of a colour fundus image is C 0. 3, r where L 1 j 0 r rj. Pr ( rj ) Ck Pr ( rj ) (3.1) k j 0 Pr(r) corresponds o he probabiliy densiy funcion of he red channel of he colour image, L indicaes he number of discree gray levels, and k refers o he bin having he mean value µr.

8 The value of C characerizes he se of very brigh images wih a large gap in heir hisograms. Figures 3.4 and 3.5 explain he affec of hisogram maching. There is a subsanial decrease in conras beween abnormaliies and he reinal background which can be observed from Figs. 3.4 and MATCHED FILTER The reinal vasculaure has he following hree imporan properies ha are useful for blood vessel analysis [18]: 1. The blood vessels may be approximaed as piecewise linear segmens as hey have small curvaures. 2. The reflecance of blood vessels is low compared o oher reinal surfaces. So he blood vessels appear darker relaive o he background. Even hough he inensiy profile of a vessel differs from anoher vessel by a small amoun, he inensiy profile of a blood vessel can be approximaed using a Gaussian shaped curve, f 2 d x, y) A{1 k exp( )}, (3.2) 2 ( 2 where d refers o he perpendicular disance beween a poin (x, у) and he sraigh line ha passes hrough he cener of blood vessel in a direcion owards is lengh, σ is he spread of he inensiy profile, A defines local background s gray level

9 (a) (d) (b) (e) (c) Fig.3.4. Fundus Image Preprocessing o Decrease he Conras beween Abnormaliies and he Reinal Background (a) Red Channel of a Fundus Image; (b) Green Channel of he Same Image; (c) Hisogram Mached Image; Gray-Level Disribuions of he Same Fundus Image (d) Red Channel; (e) Green Channel; and (f) Hisogram Mached Image. (f)

10 (a) (d) (b) (e) (c) (f) Fig.3.5. Fundus Image Preprocessing o Decrease he Conras beween Abnormaliies and he Reinal Background (a) Red Channel of a Fundus Image; (b) Green Channel of he Same Image; (c) Hisogram Mached Image; Gray-Level Disribuions of he Same Fundus Image (d) Red Channel; (e) Green Channel; and (f) Hisogram Mached Image.

11 inensiy and k indicaes he measure of reflecance of he vessel relaive o is neighborhood. 3. The widh of a blood vessel decreases as i goes radially ouward from he opic disk. Therefore, a blood vessel is defined as a dark paern having Gaussian shape cross-secion profile, piecewise conneced, and locally linear. Because of he above menioned properies, insead of maching a single inensiy profile of he blood vessel cross-secion, a considerable improvemen can be aained by maching number of cross-secions of similar profile simulaneously. A prooype of he mached filer kernel is expressed as 2 x k( x, y) exp( ) 2 2 for y L/2. (3.3) Where l indicaes he lengh of blood vessel segmen for which he blood vessel is having a fixed orienaion. The direcion of he blood vessel is considered o be aligned along he y-axis. The mached filer kernel is required o be roaed for all possible angles since a blood vessel may be oriened a any angle. Twelve 15 x 15 pixel kernels are applied o convolve wih a fundus image. A each pixel only he maximum of heir responses is reained. The resuls afer convolving mached filer kernels a differen angles wih green channel of he hisogram mached image in fig. 3.5 (c) are shown in fig.3.6. The

12 mached filer response image for he image in fig. 3.5 (c) is shown in fig. 3.7(a), where he blood vessels are significanly enhanced LOCAL RELATIVE ENTROPY THRESHOLDING WITH HISTOGRAM COMPRESSION AND TRANSLATION In order o properly segmen he enhanced blood vessel segmens in he mached filer response images, an effecive hresholding scheme is required. An efficien relaive enropy based hresholding algorihm ha akes ino accoun he spaial disribuion of gray levels is used, because some mached filer response images have complicaed relaionships or overlap beween foreground and background. Paricularly, local relaive enropy hresholding mehod [95] is implemened ha can well mainain he srucure deails in he hresholded image. The co-occurrence marix of an image is defined as a L L square marix, represened by W whose elemens are specified by he ij LL number of gray level ransiions beween all pairs is 0,1,..., L 1 G in a paricular way. Differen definiions are possible for a co-occurrence marix depending on he way he gray level i follows he gray level j. One exensively employed co-occurrence marix is an asymmeric marix ha considers only he ransiions of gray level beween wo adjacen pixels. Thus, ij is defined as follows:

13 degrees= degrees= (a) 15 degrees= (b) 30 degrees= (c) 45 degrees= (d) 60 degrees= (e) 75 (f) 90 Fig.3.6. Resuls afer Convolving Mached Filer Kernels a Differen Angles wih Green Channel of he Hisogram Mached Image Shown in Fig. 3.5 (c).

14 (g) 105 degrees= (h) 120 degrees= (i)135 degrees= (j) 150 degrees= (k)165 (l) 180 Fig.3.6. Resuls afer Convolving Mached Filer Kernels a Differen Angles wih Green Channel of he Hisogram Mached Image Shown in Fig. 3.5 (c).

15 (A) (B) Fig.3.7. (a) Mached Filer Response of he Hisogram Mached Image (b) Resul afer Local Relaive Enropy Thresholding wih HCT (c) Label Filering Resul. (c)

16 A (BB) B (BF) D (FB) C (FF) Fig.3.8. Quadrans of Co-occurrence Marix [95] ij M N m1 n1 (3.4) mn f where mn 1 if f = 0 oherwise m, n i and f m 1, n and / or m, n i and f m, n 1 j j The desired gray level i from gray level j can be aained by normalizing he oal number of ransiions in co-occurrence marix. ij p ij (3.5) L1 L 1 k0 l0 kl Le an image be hresholded by a value. The co-occurrence marix will be pariioned ino four quadrans using his hreshold, namely, A, B, C and D as shown in Fig.3.8. Here, i is assumed ha pixels having gray level above he hreshold value are assigned o foreground (corresponding o objecs). Pixels having gray level equal o or below he hreshold value are allocaed o he background.

17 The cell probabiliies in each quadran can be obained by normalizaion, p p p p pij, (3.6) P ij ij ij ij A p ij B, p ij C, p ij D PA PB PC D where P A, P B, P C and P D are he probabiliies associaed wih each quadran. Le is he seleced hreshold value. Le h ij is he ransiion probabiliy of he -hresholded image in response o pij. The corresponding cell probabiliies in each of four quadrans of a hresholded image are given by [95] h ij A q A P A 1 1 PB hij B qb (3.6.a) 1 L 1 h ij C q C P C L 1 L 1 h ij D q D P D L 1 1 The relaive enropy beween he probabiliy disribuions L1, L1 p and h ij i0, j0 ij is defined as J L 1 L1 pij ; hij i0 j0 p ij = H p ij p log h -( P A ij ij logq A P logq P logq P logq ) B B C C D D (3.7)

18 where L1, L1 ij i0, j0 H is he enropy of he probabiliy vecor specified by p ij p and is independen of. If p p / P P is given by ij AC ij A C hen he local relaive enropy hresholding LRE J p ; ij AC hij = H FF BB i, j BBFF p ij h AC log ij (3.8) Where BBFF H = i, j BBFF p ij AC refers o he enropy of he local quadrans A and C. Reducing he second erm in equaion (3.8) furher resuls in LRE J P ; ij P A qa PC qc AC hij H BB FF log log A PC A PC PA PC A PC P P P (3.9) The LRE hresholding echnique is o find a hreshold value LRE minimizes J P ; LRE ij\ AC h ij [95], ha is ha LRE min arg G J LRE Pij\ AC j ; h (3.10) Generally relaive enropy based mehods are liable o sparse image hisograms. To make he relaive enropy based mehods work efficienly, a sparse image hisogram have o be compressed o a more compac hisogram. This is known as Hisogram Compression and

19 Translaion (HCT). Assume ha an image conains 'n' number of disinc gray levels. Wihou loss of generaliy, supposing g1, g2,..., gn are he N differen gray levels which can be se in accordance wih g1 < g2 <... gn, where g1 = gmin is he smalles gray level and gn = gmax is he larges gray level. Assume ha n(gk) indicaes oal number of pixels in he fundus image ha are having gray level gk. The process of hisogram compression and ranslaion is described by mapping gk k wih HCT (gk) = k and nk = n(gk) for each 1 k N. For he mached filer response image shown in Fig. 3.7(a), he local relaive enropy based hresholding wih hisogram compression and ranslaion resul is shown in Fig. 3.7(b) where he blood vessels are clearly segmened from he background LABEL FILTERING As seen in Fig. 3.7(b), here are sill some misclassified pixels in he image. The aim of label filering is o produce a clean and complee blood vessel nework by removing misclassified pixels by using he concep of conneced componen labeling. Conneced regions correspond o individual objecs. Hence, conneced regions mus be idenified firs. Conneced componen labelling scans an image pixel by pixel. I groups all he scanned pixels ino componens based on pixel conneciviy. The label filering ries o separae he individual objecs by using eigh conneced neighborhood informaion and label propagaion [96]. Once he algorihm is execued, only he resuling componens exceeding a cerain number of pixels, e.g., 200 are

20 labeled as blood vessels. Fig. 3.7(c) shows he resuls afer label filering EXPERIMENTAL RESULTS AND DISCUSSION The proposed HMLRE mehod is esed on wo publicly available daabases of coloured fundus images and corresponding groundruh segmenaions: he DRIVE [94] and STARE [20] daabases. The DRIVE daabase conains 40 colour fundus images. The images are divided ino 20 es and 20 raining images of size 565 x 584 pixels. The fundus images have been manually segmened by hree expers. The fundus images in he raining se are segmened once, while he images in he es se are segmened wo imes, resuling in ses A and B. The STARE daabase consiss of 20 fundus images which are of size 700 x 605 pixels, 8 bis per colour channel. Among he 20 images of STARE daabase, 10 images are of paiens wih no lesions (normals) and he oher 10 images conain lesions ha obscure or confuse he blood vessel appearance in varying posiions of he fundus image (abnormals). All he images are segmened for blood vessels by wo ophhalmic expers. In order o evaluae he proposed HMLRE mehod hree performance measures are employed. The firs is ROC ha is used o plo he variaion of false posiive rae (Fp) agains rue posiive rae (Tp).

21 N p TP ; Nvessel F P N N fp (3.11) nonvessel where Np, Nfp define he number of rue posiives and false posiives. Nvessel, Nnon vessel are he oal number of blood vessel and non-blood vessel pixels in he groundruh image. The second performance measure is he area under he ROC. High area under ROC indicaes he beer deecion. The deecion is perfec, if he area under ROC is one. The hird measure is he average accuracy for all fundus images. The accuracy of an image can be found using he following expression. Accuracy Sum of he oal number of pixels, correcly classified as vessel and non-vessel pixels Toal number of pixels in he field of view of he fundus image A fundus image having severe pahology shown in Fig. 3.9(a) is uilized o compare he performance of he HMLRE mehod wih oher mehods. In all he oher blood vessel segmenaion approaches wheher i is supervised or unsupervised, he abnormaliies are misenhanced and mis-deeced as blood vessels. From Fig.3.9, i can be examined ha, he proposed HMLRE mehod avoided deecing false vessels in abnormal regions and produced reliable resuls for healhy regions successfully. Illusraive segmenaion resuls of he HMLRE mehod for a pair of fundus images from DRIVE daabase ogeher wih manual segmenaions are shown in Figs and The accuracy for he

22 (a) Reinal image wih pahology (b) Firs observer (c) Second observer (d) Resul of Soares e al.[48] (e)resul of Hoover e al.[20] (f) Resul of Jiang e al.[51] (g) Resul of proposed mehod. Fig.3.9. Comparison of Resuls on a Reinal Image having Severe Pahology shown in Fig. 3.1.

23 (a) (b) (c) (d) Fig Resul produced by he proposed HMLRE Mehod and Manual Segmenaions (Ses A and B) for an Image from he DRIVE Daabase. (a) Normal Reinal Images. (b) Segmenaion Resul of HMLRE Mehod. (c) Se A (d) Se B. firs fundus image is and for he second fundus image. The manual segmenaions of he wo observers are also shown. In Fig reinal images from STARE daabase are aken. To evaluae he performance of he HMLRE mehod, specifically a normal reinal image and a non-uniform illuminaed image wih obscure blood vessel appearance are considered. The resuls of HMLRE mehod are compared wih he groundruh images of he firs

24 (a) (b) (c) (d) Fig Resuls produced by he proposed HMLRE Mehod and Manual Segmenaions (Ses A and B) for an Image from he DRIVE Daabase. (a) Normal Reinal Images. (b) Segmenaion Resuls of HMLRE Mehod. (c) Se A (d) Se B. observer and wih he resuls of Hoover e al. [20] and Chanwimaluang e al. [24]. Firs column resuls of Fig are originaed from a normal reinal image and he second column resuls are derived from a non-uniform illuminaed reinal image wih obscure blood vessel appearance. The auhor has implemened he algorihm

25 of [24]; whereas he resuls of [20] are aken from heir websie 1. For normal reinal image he algorihms in [20] and [24] aain maximum accuracies of and respecively. However, he resul of he HMLRE mehod shows a significan improvemen wih maximum accuracy of The accuracy of he proposed HMLRE mehod for he non-uniform illuminaed image wih obscure blood vessel appearance shown in he second row is Even hough, he brighness of he fundus image reduces radially ouwards, he performance of HMLRE mehod is quie saisfacory no only in he brigh cenral region bu also in he dark ouermos one. This is owing o he use of boh red and green channels of he same fundus image, hus making proposed HMLRE mehod more robus o non-uniform illuminaion and conras. For he same image he maximum accuracies for mehods in [20] and [24] are and respecively. The ROC curves of he proposed HMLRE mehod, auhor implemenaions of he verificaion based muli hreshold probe of Jiang e al.[51], mached filer of Chaudhuri e al. [18] for boh he daabases are shown in Figs. 3.13(a) and (b). The manual segmenaions from se A of DRIVE daabase are used as groundruh. The performance human observer is measured using he manual segmenaions from se B and provides only one rue/false posiive fracion pair ha is indicaed as a poin in he ROC graph (Fig. 3.13(a)). The firs observer s manual segmenaions are used as 1 hp://

26 Fig Resuls Produced by he Proposed HMLRE Mehod for Images from he STARE Daabase. Firs Row: Example Images; Second Row: Hand-Labeled Groundruh ([20]); Third Row: Resuls of Hoover e al. [20]; Fourh Row: Resuls of Chanwimaluang e al. [24]; Fifh Row: Resuls of HMLRE Mehod.

27 rue posiive rae rue posiive rae Proposed Mehod Jiang e al. 0.1 Chaudhari e al. Se B false posiive rae Fig (a) ROC Curve for Classificaion on he DRIVE Daabase. The poin marked as corresponds o se B, he second se of manual segmenaions. The HMLRE Mehod has Az = Proposed Mehod Jiang e al. 0.1 Chaudhari e al. 2nd observer false posiive rae Fig (b) ROC curve for classificaion on he STARE daabase. The poin marked as corresponds o he second observer s manual segmenaions. The mehod has Az =

28 Table 3.1 Resuls for Differen Blood Vessel Exracion Mehods and a Second Human Observer. Segmenaion Mehod Az DRIVE Accurac y Daabase Az STARE Accurac y Commen Saal e al Supervised Soares e al Supervised HMLRE mehod Jiang e al Chaudhuri e al * * Lam e al. * * Unsupervise d Unsupervise d Unsupervise d Unsupervise d 2 nd. observer * * * *- No available groundruh for he STARE daabase, and he rue/false posiive fracion pair of second observer is shown as a poin on he ROC graph (Fig. 3.13(b)). Table 3.1 compares he proposed approach wih he mos recen mehods in erms of area under ROC and accuracy. The performance of he vessel segmenaion mehod of Saal e al. [49] is aken from [48]. The performance of he vessel segmenaion mehod in Lam e al. is aken from [97]. The area under ROC (Az) of he HMLRE mehod is less when compared o supervised mehods [48-49]. The reason is ha supervised mehods learn from he manual segmenaions and hey are able o give in accurae resuls near he edge of groundruh region. Hence in he ROC curves, he rise in he rue posiive rae does

29 no lead o a large rise in he false posiive rae and he supervised mehods are able o yield a beer Az value. In conras o he supervised mehods, he proposed HMLRE mehod is more robus in he abnormal regions and can avoid segmening false vessels in hese regions. I is able o yield a soluion very closer o he groundruh. Thus he HMLRE mehod produces he highes accuracy among ohers, including supervised mehods and gives a ROC curve closes o he rue/false posiive fracion pair from he second observer. The proposed HMLRE mehod is implemened in MATLAB 7.4 on a core 2 Duo 1.8 GHz PC wih 1GB memory. The blood vessel segmenaion for an image from DRIVE or STARE daabase requires abou 8 seconds, while Saal s mehod needs 900 seconds on a 1GHz PC and Soare s mehod requires 180 seconds on a 2GHz PC CONCLUSIONS In his chaper, a novel approach for he segmenaion of blood vessels in digial fundus images is presened. The proposed HMLRE mehod akes ino accoun he advanages of boh red and green channels of a fundus image and he relaive local enropy based hresholding. The inensiy informaion of he red channel is uilized for wo reasons: 1) To enhance he visual appearance of fundus images in case of non-uniform illuminaion and 2) To aain beer performance of blood vessel segmenaion. Combining he advanages of red and green channels i.e. brighness in he red channel and high conras in he green channel, resuls in he reducion of he conras beween lesions and he fundus background. Local relaive enropy

30 hresholding algorihm, which akes ino accoun he spaial disribuion of gray levels, performs efficienly in disinguishing enhanced vessel segmens from he background as i can mainain he srucure deails of an image. Performance evaluaed in erms of area under ROC and accuracy shows ha he proposed unsupervised mehod is beer han he exising unsupervised approaches and maches he supervised approaches. The HMLRE mehod performs very well in segmening blood vessels in pahological regions and also exracing hinner vessels even in low conras regions as he inensiy informaion of boh red and green channels is used. However, Fig shows ha, here is sill room for improvemen for deecing he finer vessels. Anoher difficuly of he proposed mehod is ha in some images border of he opic disk is mis-deeced as blood vessel. The fuure work aims o solve hese challenges. I is essenial o obain large daases wih groundruh for developing robus soluions o be used for vessel segmenaion ha can be employed in diabeic screening programmes. However, his is difficul o realize as he groundruh generaion is a edious process ha demands paience. As supervised approaches need par of he available daase o be employed for raining o segmenaion, i resuls in decrease of he acual es se size. These approaches can make i difficul o fully analyse and sandardize differen mehods and obsruc he idenificaion of robus mehods for deploymen in mass screening programmes. Unsupervised echniques for segmenaion, such as he proposed HMLRE mehod, are hence quie

31 aracive in his scenario. The HMLRE mehod exhibis he following feaures: low compuaional cos fas high accuracy robusness wih respec o camera, lighing condiions and he presence of pahologies.