Artery Vein Classification DACO Final Project 2017 Members: Gabriel Lopes João Silveira Paulo Maia Tiago Gonçalves
FEUP 2017/2018
This project forked from tiagofilipesousagoncalves/information-theory-assignments
Artery Vein Classification DACO Final Project 2017
Talvez seja melhor fazer uma identificação mais robusta do que a que eu fiz com a hit and miss transform.
Fiz deste modo: https://stackoverflow.com/questions/16241708/hit-and-miss-transform-for-detecting-branched-point-and-endpoint-in-scikit-image
Ver o artigo "A new tracking system for the robust extraction of retinal vessel structure"
Pontos principais
Alternativa nº2, mais geral, mas que pode dar menos falsos positivos que a minha:
Segment start and end positions are determined as follows. Each of the centerline pixels on the vessel skeleton is analyzed within its 3x3 neighborhood, and branch and crossing points are detected as centerline pixels with more than 2 neighbors. The detection of vessel end points is required for the graph search and they are determined as the centerline pixels with only one neighbor. (2014 - automated method...)
Meter aqui características e artigos que as referiram
**Parte prática: como fazer esses cálculos?
The problem is complicated by the similarity in the descriptive features of these two structures and by the contrast and luminosity variability of the retina. We developed a new algorithm for classifying the vessels, which exploits the peculiarities of retinal images. By applying a divide et impera approach that partitioned a concentric zone around the optic disc into quadrants, we were able to perform a more robust local classification analysis
Classifica bem a região em redor do disco ótico, que tem mais informação e depois propaga as labels. Acho que é a melhor maneira de fazer - se adicionarmos os pontos mais distantes do disco otico, que sao semelhantes nas veias e nas arterias, vao estar a criar um enviesamento do classificador nessa direção, para alguns classificadores. Para os SVM acho que é mais robusto, visto que só importa os support vectors , e ao acrescentar mais pontos nao modifica o resultado.
After classification each pixel has been assigned to either the artery or the vein class. To improve the result we can infer some meta-knowledge. The concept is that all pixels belonging to the same vessel section between two crossings must also belong to the same class. To use this knowledge we now need the skeleton sections, that have been calculated during the preprocessing step. We add up the confidence of all pixels in one section for belonging to the artery and for belonging to the vein class. Then we choose the class label with the higher value.
In this way the pixels classified with high reliability will influence the final classification of the whole section to a larger extent than the ones with poor reliability. This method is especially helpful for longer vessel sections that start somewhere near the center and end in the outer regions of the image, since in this case the pixels far away from the center are usually very hard to classify correctly
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