Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images

Abstract Introduction A new method for segmenting and quantifying the macular area based on morphological alternating sequential filtering (ASF) is proposed. Previous studies show that persons with diabetes present alterations in the foveal avascular zone (FAZ) prior to the appearance of retinopathy. Thus, a proper characterization of FAZ using a method of automatic classification and prediction is a supportive and complementary tool for medical evaluation of the macular region, and may be useful for possible early treatment of eye diseases in persons without diabetic retinopathy. Methods We obtained high-resolution retinal images using a non-invasive functional imaging system called Retinal Function Imager to generate a series of combined capillary perfusion maps. We filtered sequentially the macular images to reduce the complexity by ASF. Then we segmented the FAZ using watershed transform from an automatic selection of markers. Using Hu's moment invariants as a descriptor, we can automatically classify and categorize each FAZ. Results The FAZ differences between non-diabetic volunteers and diabetic subjects were automatically distinguished by the proposed system with an accuracy of 81%. Conclusion This is an innovative method to classify FAZ using a fully automatic algorithm for segmentation (based on morphological operators) and for the classification (based on descriptor formed by Hu's moments) despite the presence of edema or other structures. This is an alternative tool for eye exams, which may contribute to the analysis and evaluation of FAZ morphology, promoting the prevention of macular impairment in diabetics without retinopathy.

Heartbeat classification system based on neural networks and dimensionality reduction

Abstract Introduction This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats. Methods A pre-processing step was performed on the electrocardiograms (ECG) for baseline removal. Next, a QRS complex detection algorithm was implemented to detect the heartbeats, which contain the primary information that is employed in the classification approach. Next, ECG segmentation was performed, by which a set of features based on the RR interval and the beat waveform morphology were extracted from the ECG signal. The size of the feature vector was reduced by principal component analysis. Finally, the reduced feature vector was employed as the input to an artificial neural network. Results Our approach was tested on the Massachusetts Institute of Technology arrhythmia database. The classification performance on a test set of 18 ECG records of 30 min each achieved an accuracy of 96.97%, a sensitivity of 95.05%, a specificity of 90.88%, a positive predictive value of 95.11%, and a negative predictive value of 92.7%. Conclusion The proposed approach achieved high accuracy for classifying ECG heartbeats and could be used to assist cardiologists in telecardiology services. The main contribution of our classification strategy is in the feature selection step, which reduced classification complexity without major changes in the performance.

Medición de la microcirculación retiniana luego del tratamiento antihipertensivo / Measuring retinal circulation after antihypertensive treatment

Background and objective: With the development of image processing techniques, it has become possible to measure the changes in retinal vessels of hypertensive patients by means of eye fundus photographs. Patients and method: In this paper we aim to classify retinal vessels automatically into arterioles and venules. In order to do so, we have compared three different strategies based on the colour of the pixels in images through an analysis of 78 hypertensive patients' eye fundus images. The first strategy classifies all the vessels by applying a clustering algorithm. The second divides the retinal image into four quadrants and classifies the vessels that belong to the same quadrant independently from the rest of the vessels. The third strategy classifies the vessels by dividing the retinal image into four quadrants that are rotated inside the mentioned image. Results: The third strategy was the one that obtained the best results, since it minimizes the number of unclassified vessels. In the initially analysed set of 20 images, we correctly classified 86.53% of the vessels, and this percentage remains similar in a set of 58 images examined by three medical experts. This confirms the validity of the method that automatically calculates the arteriovenous ratio (AVR).Conclusion: Our results are an improvement on those previously described in the bibliography, reducing the number of non-classified vessels. Furthermore, the method entails low computational costs.

Fundamento y objetivo: El desarrollo de técnicas de procesado de imágenes ha devuelto interés para poder medir de una forma objetiva los cambios en la estructura microvascular del hipertenso a través de las fotografías digitales del fondo de ojo. Pacientes y método: Para clasificar de forma automática los vasos de la retina en arteriolas y vénulas, con una elevada precisión, hemos comparado tres estrategias diferentes basadas en la información del color de los pixeles de la imagen del fondo de ojo, analizando 78 imágenes de fondo de ojo de hipertensos. La primera estrategia clasificaría todos los vasos aplicando un algoritmo de agrupamiento. La segunda divide la retina en cuatro cuadrantes y clasifica los vasos que pertenecen al mismo cuadrante independientemente del resto de los vasos. La tercera estrategia clasifica los vasos dividiendo la retina en cuadrantes que son rotados. Resultados: La mejor estrategia resultó la tercera porque minimiza el error y el número de vasos no clasificados. La característica vectorial más determinante está basada en la media o la mediana del componente gris del espacio de color RGB. Para las 20 imágenes inicialmente analizadas hemos clasificado correctamente el 86.53% de los vasos, y este porcentaje permanece similar en el grupo de 58 imágenes examinadas por tres expertos, lo que confirma la validez del método, para el cálculo del índice arteriovenoso de forma automática. Conclusión: Nuestros resultados son superiores a los descritos previamente, reduciendo además el número de vasos no clasificados. Por otro lado, el costo computacional del método es bajo

Discriminação de variedades de citros em imagens CCD/CBERS-2 / Discrimination of citrus varieties using CCD/CBERS-2 satellite imagery

O presente trabalho teve o objetivo de avaliar as imagens CCD/CBERS-2 quanto à possibilidade de discriminarem variedades de citros. A área de estudo localiza-se em Itirapina (SP) e, para este estudo, foram utilizadas imagens CCD de três datas (30/05/2004, 16/08/2004 e 11/09/2004). Um modelo que integra os elementos componentes da cena citrícola sensoriada é proposto com o objetivo de explicar a variabilidade das respostas das parcelas de citros em imagens orbitais do tipo CCD/CBERS-2. Foram feitas classificações pelos algoritmos Isoseg e Maxver e, de acordo com o índice kappa, concluiu-se que é possível obterem-se exatidões qualificadas como muito boas, sendo que as melhores classificações foram conseguidas com imagens da estação seca.

This paper was aimed at evaluating the possibility of discriminating citrus varieties in CCD imageries from CBERS-2 satellite ("China-Brazil Earth Resouces Satellite"). The study area is located in Itirapina, São Paulo State. For this study, three CCD images from 2004 were acquired (May 30, August 16, and September 11). In order to acquire a better understanding and for explaining the variability of the spectral behavior of the citrus areas in orbital images (like as the CCD/CBERS-2 images) a model that integrates the elements of the citrus scene is proposed and discussed. The images were classified by Isoseg and MaxVer classifiers. According to kappa index, it was possible to obtain classifications qualified as 'very good'. The best results were obtained with the images from the dry season.

Classification of Continuous Heart Sound Signals Using the Ergodic Hidden Markov Model / 대한의료정보학회지

Recently, hidden Markov models (HMMs) have been found to be very effective in classifying heart sound signals. For the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. However, the manual segmentation will be practically inadequate in real environments. Although, there have been some research efforts for the automatic segmentation, the segmentation errors seem to be inevitable and will result in performance degradation in the classification. To solve the problem of the segmentation, we propose to use the ergodic HMM for the classification of the continuous heart sound signal. In the classification experiments, the proposed method performed successfully with an accuracy of about 99(%) requiring no segmentation information.