Objective: To assess whether the methodological changes of this new algorithm improves
the results of a previously presented strategy.
Methods: We enhance the image and filter out the green channel of the digital color retinog-
raphy. Multitolerance thresholding was applied to obtain candidate points and make a seed
growing region by varying intensities. We took 15 characteristics from each region to train a
fuzzy Artmap neural network using 42 retinal photographs. This network was then applied
in the study of 11 good quality retinal photographs included in the diabetic retinopathy early
detection screening program, with initial stages of retinopathy, obtained with the Topcon
NW200 non-mydriatic retinal camera.
Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The
algorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microa-
neurysm and 135 false positives. The sensitivity is improved compared to the previous
algorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image.
Conclusions: The new algorithm is better than the previous one, but there is still room for
improvement, especially in the initial determination of seed

Acha Piñero, Begoña

Alemany, P.

Failde, I.

Fondón García, Irene

Jiménez, S.

Núñez, F.J.

Serrano Gotarredona, María del Carmen

English

idUS. Depósito de Investigación Universidad de Sevilla

a r c h s o c e s p o f t a l m o l . 2 0 1 2;8 7(9):284–289ARCHIVOS DE LA SOCIEDADESPAÑOLA DE OFTALMOLOGÍAwww. e l sev ier .es /o f ta lmologiaOriginal articleAutomated  detection  of microaneurysms  by  using  regiongrowing and  fuzzy  artmap  neural  network,S. Jiméneza,b,∗, P. Alemanya,b, F.J. Núñezc, I. Fondónc, C. Serranoc, B. Achac, I. Faildeda Servicio de Oftalmología, Hospital Universitario Puerta del Mar, Cádiz, Spainb Departamento de Cirugía, Facultad de Medicina, Universidad de Cádiz, Cádiz, Spainc Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spaind Departamento de Biomedicina, Biotecnología y Salud Pública, Facultad de Medicina de Fisioterapia, Universidad de Cádiz, Cádiz, Spaina  r  t  i  c  l  e  i  n  f  oArticle history:Received 28 January 2012Accepted 8 April 2012Available online 24 October 2012Keywords:Computer aided diagnosisFundus photographyDiabetic retinopathy screeningMicroaneurysmNeural networka  b  s  t  r  a  c  tObjective: To assess whether the methodological changes of this new algorithm improvesthe results of a previously presented strategy.Methods: We  enhance the image  and filter out the green channel of the digital color retinog-raphy. Multitolerance thresholding was applied to obtain candidate points and make a seedgrowing region by varying intensities. We  took 15 characteristics from each region to train afuzzy Artmap neural network using 42 retinal photographs. This network was then appliedin  the study of 11 good quality retinal photographs included in the diabetic retinopathy earlydetection screening program, with initial stages of retinopathy, obtained with the TopconNW200 non-mydriatic retinal camera.Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. Thealgorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microa-neurysm and 135 false positives. The sensitivity is improved compared to the previousalgorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image.Conclusions: The new algorithm is better than the previous one, but there is still room forimprovement, especially in the initial determination of seeds.©  2012 Sociedad Española de Oftalmología. Published by Elsevier España, S.L. All rightsreserved.Detección  automatizada  de  microaneurismas  mediante  crecimientode  regiones  y  red  neuronal  Fuzzy  Artmapr  e  s  u  m  e  nPalabras clave:Diagnóstico asistido por ordenadorRetinografíaDetección de retinopatía diabéticaObjetivo: Comprobar si las modificaciones metodológicas de este nuevo algoritmo mejoranel  resultado de otra estrategia presentada anteriormente.Métodos: Se realza y filtra la imagen negada del canal verde de la retinografía digitalen  color. Se aplica una umbralización multitolerancia para obtener puntos candidatos Please cite this article as: Jiménez S, et al. Detección automatizada de microaneurismas mediante crecimiento de regiones y redneuronal Fuzzy Artmap. Arch Soc Esp Oftalmol. 2012;87:284–9. This paper was presented at the research session of the 86 SEO Congress held in Madrid on September 22, 2010.∗ Corresponding author.E-mail address: soledadjimenez@ono.com (S. Jiménez).2173-5794/$ – see front matter © 2012 Sociedad Española de Oftalmología. Published by Elsevier España, S.L. All rights reserved.a r c h s o c e s p o f t a l m o l . 2 0 1 2;8  7(9):284–289 285MicroaneurismasRed neuronaly en cada semilla se realiza un crecimiento de regiones por variación de intensidades. Setoman 15 características de cada región y entrenamos una red neuronal Fuzzy Artmap con42  retinografías. Se aplica la red en el estudio de 11 retinografías del programa de detecciónprecoz de retinopatía diabética, de buena calidad, con lesiones iniciales, obtenidas con elretinógrafo no midriático Topcon NW200.Resultados: Dos oftalmólogos experimentados detectan 52 microaneurismas en las 11imágenes. El algoritmo detecta 39 microaneurismas y 3.752 regiones más, confirmando38  microaneurismas y 135 falsos positivos. La sensibilidad ha mejorado respecto al algo-ritmo anterior del 60,53 al 73,08%. Los falsos positivos has disminuido de 41,8 por imagen a12,27.Conclusiones: El nuevo algoritmo presenta indudables mejoras respecto al anterior, pero aúnse  puede perfeccionar, sobre todo en la determinación inicial de semillas.©  2012 Sociedad Española de Oftalmología. Publicado por Elsevier España, S.L. Todos losIDa7ienabIedmtoaTmdMATstiotbntroductioniabetic retinopathy is the most frequent cause of newdult blindness cases for age groups between 20 and4 years.1 Diabetic retinopathy detection programs focus ondentifying injuries that point to the disease (among oth-rs, microaneurysms) in color digital photographs taken withon-midriatic retinographs in the target population.2 The sizend color of microaneurysms make them easily identifiabley primary care physicians involved in detection programs.n addition, the localization process is tedious and slow,ven for specialists. For these reasons it is necessary toevise an automated system to quickly and efficiently detecticroaneurysms. To this end, algorithms were developedo enhance microaneurysms candidate injuries, eliminatether ocular fundus elements and establish different char-cteristics to classify candidates as true microaneurysms.3–6his paper presents an improved algorithm for detectingicroaneurysms in color digital photographs of patients withiabetic retinopathy and the diagnostic usefulness thereof.aterials  and  methodslgorithm  developmenthe method utilized in this paper is summarized in thechema illustrated in Fig. 1. During the preprocessing phasehe green negated channel (gneg) is obtained from the RGBmage.  In this channel the shiny elements that represent theptic disk and exudates (if any) are segmented by histogramhresholding, obtaining a binary image  where the pixelselonging to said elements have a value of 1 and the remainderPreprocessing Seed selection Rregion growth ClassificatgionFig. 1 – Algorithm flow diagram.derechos reservados.will have a value of zero. Subsequently, the vascular tree is seg-mented with a multi-tolerance thresholding method7 wherea threshold condition depending on the surface occupied bythe vascular tree is added. The result is a binary image  wherethe pixels belonging to the vascular tree have a value of 1and the rest of the image  has a value of zero. Said binary imageis combined with the shiny elements image  to attain a binarymask (mbin).After the vascular tree segmentation, gneg is submittedto a polynomic contrast enhancing process which producesthe C(gneg) image.  Subsequently, the image  is submitted to2 successive filterings. Firstly, a 3 × 3 pixel Gaussian filter isapplied having a value of  = 2. Then this image  is submittedto a high step filtering to remove the fundus and maintain theelements that constitute a sudden change in image  intensity,thus obtaining the standardized image  <HP(C(gneg))>. Finally,the vascular tree and the shiny elements detected in mbin areremoved from the image,  which causes that the remainingpixels have a value of zero. Fig. 2 illustrates the preprocessingschema and Fig. 3 shows an example with some of the imagesobtained with said preprocessing.The next step consists of illuminating the vascular tree andshiny elements from the <HP(C(gneg))> image,  so that all thepixels present in mbin are 0.In the Seed Selection phase, a new multi-tolerance thresh-olding of the image  is carried so render a maximum of 1000regions which could be microaneurysm seeds. During thresh-olding, the pixel with highest intensity in each segmentedregion is marked as a seed candidate. These potential seedsare subsequently validated by calculating for each the statis-tical tail ratio that provides information about the distributionof intensities in the histogram of a window centered on thepixel being studied.8In each selected seed a region growth is applied in whichneighboring pixels are added having an intensity above themean value plus the typical deviation of a window centeredon the seed having a size of 10 × 10 pixels. For the growingregion to be valid it must not have a surface exceeding 30 pixelsbecause in our database and at the resolution we work withthere is no microaneurysm having an extension above saidvalue (Fig. 4).In order to classify regions, 15 characteristics are calculatedfor each grown seed−numbered from 1 to 15–of which 4 are286  a r c h s o c e s p o f t a l m o l . 2 0 1 2;8 7(9):284–289+  -RGB imageGreennegatedchannelBinary mask(tree and shinyelements)ContrastenhancementGaussianfilteringAveragingfilteringFiltered imageFig. 2 – Preprocessing schema.calculated on the basis of the binary representation of eachgrown region, 4 on the basis of the gneg image,  4 on the basisof the C(gneg) image  and 3 on the basis of the <HP(C(gneg))>image. The binary image  only counts with pixels having val-ues of zero and one, without quantifiable intensity of contrastdifferences. It is the most adequate image  to determine mor-phological parameters. The 4 characteristics are:1) Surface (Si): sum of pixels belonging to region i.Fig. 3 – (a) gneg, (b) mbin, (c) C(gn2) Aspect ratio: the result of dividing the larger diameter bythe smaller diameter of the region.3) Perimeter (Pi): the number of pixels located at the edge ofregion i.4) Circularity (Ci): this characteristic provides informationabout the shape of region i, and is calculated by means ofCi = P2i /(4Si).The 4 characteristics taken on the basis of regions in gneg are:eg), and (d) <HP(C(gneg))>.a r c h s o c e s p o f t a l m o l . 2 0 1 2;8  7(9):284–289 287Fig. 4 – (a) Cutout of C(gneg). (b) Result of the region growth in the cutout: the lighter points of the seed pixels detected by thealgorithm; the gray extensions are the candidate points with the final region growth. The microaneurysms detected by thespecialist are shown within red squares.12341234IadT123wwanagRTt) Mean intensity A (<IiA>): the sum of the pixel intensities ofregion i divided by the surface of said region.) Correlation with Gaussian A: provides information aboutthe similarity of region i with a Gaussian structural elementof the same size of region i.) Contrast A: it measures the difference between the meanintensity of region i pick cells and adjacent pixels.) Fundus A mean intensity (<IfA>): the mean of pixelsbelonging to a window having a size of 15 × 15 pixels cen-tered on the initial seed of each region.The characteristics calculated on the basis of C(gneg) are:) Mean intensity B (<IiB>).) Correlation with Gaussian B.) Contrast B.) Fundus B mean intensity (<IfB>).n order to obtain the <HP(C(gneg))> image,  a high filter waspplied to eliminate the fundus. For this reason, this imageoes not allow an assessment of the mean fundus intensity.he determined characteristics in this image  are:) Mean intensity C (<IiC>).) Correlation with Gaussian C.) Contrast C.A selection was made to determine characteristicsith greater discriminating potential by means of for-arded sequential selection and backward sick when she’lllimentation. For classification, a Fuzzy Artmap9 typeeuronal network was utilized, which was trained with group of images to subsequently classify a differentroup.etinographshe baseline was a database comprising 42 retinographs ofhe A Diabetic Retinopathy Early Detection Plan, the initialassessment of which must be carried out by primary healthcare physicians (PHP) of the hospital area for training theneuronal network. Said images were reviewed by 2 of theauthors (SJ and PA) who pointed out 204 microaneurysms.Subsequently, 11 successive images were taken of 11 patientsfrom the same early detection program diagnosed with dia-betic retinopathy by the PHP and reviewed by the authorsfor evaluating the algorithm. The images were taken witha Topcon NW-200 retinograph and stored in the JPG for-mat.Computer  equipmentThe algorithm was developed with the MATLAB 7.6.0 (R2008a;MathWorks) mathematical software on an HP Compaq dc7600Convertible computer with an Intel® Pentium® 4 processorrunning on a CPU of 3.20 GHz and with 0.99 GB of RAM mem-ory.StatisticsThe average sensitivity of the algorithm and the number offalse positives for each image  was calculated.ResultsInitially, 42 images with 204 microaneurysms were selected.During the seed selection process, seeds were placed in167 microaneurysms. After region growth, the algorithm seg-mented 50,184 regions, of which 153 were microaneurysms(75%), and the remaining 15,031 were false positives. Thesewere the regions of which the 15 characteristics discussedabove were calculated. During the neuronal network trainingthe best results were obtained with characteristics number 4,7, 9, 13 and 15, obtaining an average sensitivity of 92.85% onthe classification group, with the classifier exhibiting a hit rateof 92.45%.Once the network was trained, the group of 11 retinographscontaining 52 diagnosed microaneurysms was classified.l m o lr288  a r c h s o c e s p o f t a After the seed selection process and regional growth,a total amount of 32 segmented microaneurysms wereobtained in addition to 3752 segmented regions that werenot microaneurysms. After classifying said regions, 38segmented microaneurysms were obtained together with135 false positives, which translates into a sensitivity of73.08% and a mean value of 12.27 false positives perimage.DiscussionIn the past various methods have been proposed for automat-ically detecting microaneurysms.10–12 However, said methodshave always been assessed with proprietary image  databaseswhich were different for each author and had different techni-cal characteristics and reference parameters. Niemeijer et al.carried out an online experience in which they made avail-able to various research groups an images database takenwith non-midriatics retinographs, stored in JPEG format inorder to apply various algorithms and with results recordedin a homogeneous manner by means of FROC curves.2 In saidstudy, they differentiated 4 types of microaneurysms. On thebasis of their size and visibility these are: minimum, mediumand evident. According to location, microaneurysms close tovessels are considered in a specific manner. A specific assess-ment was made for each microaneurysm type and an overallassessment in which the detection of the 4 types was takenjointly. In the 5 detection methods, executed by 5 researchgroups that accepted the online challenge, a direct relation-ship was appreciated between the greater sensitivity of thealgorithm and the increase in the number of false microa-neurysms per image.  When the algorithms analyzed the totalamount of microaneurysms, over 10 false microaneurysmswere detected by image  to raise sensitivity values between 50%and 60%.On the basis of the images supplied in this study,the method developed by our research group presentedin the 2009 CASEIB7 was applied, obtaining sensitivityresults of 16.53% and an average of 41.8 false positivesper image.  This represents a significant improvement vis-à-vis the previous methods and validity similar to thatpresented in the various methods of the online chal-lenge.The identification of small second or third order arte-rioles and venules as isolated segments is the most usualsource of false microaneurysms. For this reason, the cor-rect segmentation of the vascular tree is one of the keysteps in automatic detection algorithms. In turn, the seg-mentations depend on the notoriety of the structuresafter completing image  preprocessing and initial process-ing.The next step in our research will focus on increasing thesystems sensitivity by means of analyzing new approachesin region segmentation because this is where sensitivity isreduced the most. Our research continues to diminish therate of false positives per image,  including new pre- andpost-processing methods that are currently in experimenta-tion. . 2 0 1 2;8 7(9):284–289ConclusionsWith the algorithm presented in this paper, significantimprovements have been obtained in the automatic detectionof initial diabetic retinopathy injuries, increasing the sensitiv-ity of the previous algorithm for microaneurysms from 60.53%to 73.08%, and diminishing the number of false positives perimage  from 41.08 to 12.27. However, the method still requiresadditional improvements so that the design tool can be com-pared to human detection with sufficient reliability to becomea useful tool in clinical diabetic retinopathy detection pro-grams.FundingFunded by the Health Research Fund of Research Projects FISETES-PI07/90379, ETES-PI07/90373.Conflict  of  interestsNo conflict of interests has been declared by the authors. e  f  e  r  e  n  c  e s1. Fong DS, Aiello L, Gardner TW, King GL, Blankenship G,Cavallerano JD. Diabetic retinopathy. Diabetes Care.2003;26:99–102.2. Niemeijer M, van Ginneken B, Cree MJ, Mizutani A, Quellec G,Sanchez CI, et al. Retinopathy online challenge: automaticdetection of microaneurysms in digital color fundusphotographs. IEEE Trans Med Imaging. 2010;29:185–95.3.  Spencer T, Phillips RP, Sharp PF, Forrester JV. Automateddetection and quantification of microaneurysms influorescein angiograms. Graefes Arch Clin Exp Ophthalmol.1992;230:36–41.4.  Cree MJ, Olson JA, McHardy KC, Sharp PF, Forrester JV. A fullyautomated comparative microaneurysm digital detectionsystem. Eye. 1997;11:622–8.5.  Sánchez CI, Hornero R, Mayo A, García M. Mixturemodel-based clustering and logistic regression for automaticdetection of microaneurysms in retinal images. In:Karssemeijer N, Giger L, editors. SPIE medical imaging 2009:computer-aided diagnosis. Proceedings of SPIE, vol. 7260.2009. p. 72601M1–M7.6.  Zhang B, Wu  X, You J, Li Q, Karray F. Hierarchical detectionof  red lesions in retinal images by multiscale correlationfiltering. In: Karssemeijer N, Giger L, editors. SPIE medicalimaging 2009: computer-aided diagnosis. Proceedings of SPIE,vol. 7260. 2009. p. 72601L1–L6.7.  Núñez FJ, Serrano C, Acha B, Fondón I, Jiménez S, Alemany P.Detección Automática de Microaneurismas en Retinografíaspara Diagnóstico Precoz de Retinopatía Diabética. CASEIB.2009;27:581–4.8.  Acha B, Serrano C, Rangayyan RM. Detection ofmicrocalcifications in mammograms using 2d predictionfiltering and a new statistical measure of the right tail weight.Proc Embec. 2005:3112–7.9. Carpenter A. Fuzzy ARTMAP: a neural network architecturefor  incremental supervised learning of analogmultidimensional maps. IEEE Trans Neural Netw.1992;3:698–713. o l . 211a r c h s o c e s p o f t a l m0. Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MSA,Abramoff MD. Automatic detection of red lesions in digitalcolor fundus photographs. IEEE Trans Med Imaging.2005;24:584–92.1. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, MensahE,  Lal S, et al. Automated detection of diabetic retinopathy on1 0 1 2;8  7(9):284–289 289digital fundus images. Diabet Med. 2002;19:105–12.2. Quellec G, Lamard M, Josselin PM,  Cazuguel G, Cochener B,Roux C. Optimal wavelet transform for the detection ofmicroaneurysms in retina photographs. IEEE Trans MedImaging. 2008;27:1230–41.

Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

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Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

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