RESUMO
Introduction: Ingredients of oily fish include a large amount of polyunsaturated fatty acids, which are important elements in various metabolic processes of humans, and have also been used to prevent diseases. However, in an attempt to reduce cost, recent developments are starting a replace the ingredients of fish oil with products of microalgae, that also produce polyunsaturated fatty acids. To do so, it is important to closely monitor morphological changes in algae cells and monitor their age in order to achieve the best results. This paper aims to describe an advanced vision-based system to automatically detect, classify, and track the organic cells using a recently developed SOPAT-System (Smart On-line Particle Analysis Technology), a photo-optical image acquisition device combined with innovative image analysis software. Methods The proposed method includes image de-noising, binarization and Enhancement, as well as object recognition, localization and classification based on the analysis of particles’ size and texture. Results The methods allowed for correctly computing cell’s size for each particle separately. By computing an area histogram for the input images (1h, 18h, and 42h), the variation could be observed showing a clear increase in cell. Conclusion The proposed method allows for algae particles to be correctly identified with accuracies up to 99% and classified correctly with accuracies up to 100%.
RESUMO
No ano 2010, doenças cardiovasculares (CVD) causaram 33% do total das mortes no Brasil. Tomografia Ótica Coerente Intravascular (IOCT) é uma tecnologia que oferece imagens in vivo para detecção e monitoramento da progressão de CVD. O exame de IOCT permite mais precisão no diagnóstico; contudo, ainda é pequena a variedade de métodos quantitativos aplicados a IOCT na literatura, em comparação à outras modalidades relacionadas. Portanto neste trabalho é proposto um método de segmentação do lúmen, baseado em uma combinação de Fuzzy Connectedness, com múltiplas funções de afinidade, e Operações Morfológicas. As funções de afinidade usadas neste trabalho são: (I) Clássica, (II) Pesos Dinâmicos e (III) Bhattacharyya. Esta última é baseada no coeficiente de Bhattacharyya, utilizado habitualmente para speckle tracking. Primeiro, características não desejadas da imagem são atenuadas. Depois, informações da parede do vaso são obtidas utilizando Fuzzy Connectedness e um processo de binarização dinâmico. Finalmente, operações morfológicas são realizadas para melhorar o lúmen segmentado. Para avaliar o método proposto, um conjunto de 130 imagens advindas de humanos, porcos, e coelhos foram segmentadas e comparadas com seus respectivos "Gold Standards" feitos por especialistas. Uma média de verdadeiros positivos (TP%) = 98,08 e de falsos positivos (FP%) = 2,34 foram obtidas. Com isso, o método proposto resultou em uma maior eficácia do que os estudos publicados anteriormente, encorajando seu uso.
In 2010 cardiovascular disease (CVD) caused 33% of the total deaths in Brazil. Intravascular Optical Coherent Tomography (IOCT) is an imaging technology that provides in vivo detection and monitoring of the progression of coronary heart disease. IOCT exam allows more accurate diagnoses; nonetheless, the set of quantitative methods applied to IOCT in the literature is small compared to other related modalities. Therefore, the proposed approach presents a lumen segmentation method, based on a combination of Fuzzy Connectedness, with multiple affinity functions, and Morphological Operations. The affinity functions used in this work are: (I) classical, (II) Dynamic weights (III) Bhattacharyya. The latter is based on the Bhattacharyya coefficient, commonly used for speckle tracking. Firstly, unwanted features of the image are attenuated. Then, vessel-wall information is obtained using Fuzzy Connectedness and dynamic binarization process. Finally, morphological operations are performed to improve the segmented lumen. To evaluate the proposed method, a set of 130 images from humans, pigs and rabbits were segmented and compared to their corresponding gold standard made by experts. An average of true positive (TP%) = 98.08, and false positive (FP%) = 2.34 were obtained. Hence, the use of the proposed method is suggested since it has yielded higher efficiency than previously published studies.