Bonsoir tout le monde. J’aimerais savoir est-ce que c’est possible de mesurer la précision de la segmentation par la méthode du K-means effectué par le plugin Beat/ Cluster Image
Precision with respect to… what? the labels of k-means clustering are arbitrarily assigned.
You need a ground truth or gold standard for that. If you have that kind of data/images, then you could use the Dice index and see for that particular segmentation how well it matched with a known category, but running k-means several times can result in your known category to have a different cluster label.
Alternatively you need to work out, by other means what your kth class might correspond to.
Bonjour, je m’excuses du retard en réalité ce que veux mesurer c’est le taux d’erreur de classification
What I said: use the Dice index:
However you cannot rely on k-means to return (let’s say) consistently 3 classes with the same label every time.
If you have: background, soft tissue and hard tissue with labels 0, 1, 2 and run k-means several times, you might end up with the 3 classes labelled as 2, 1, 0, or 1, 0, 2 or 2, 0, 1 etc, The labels are just labels, not classes. That means that you cannot rely on k-means to detect a class of your choice. Once you have done the clustering, you need to work out what the labels mean, or assign the label correspondences manually. K-means is not a classifier, it is a clustering procedure.
D’accord merci bien.