Method, electronic apparatus, and computer readable medium of constructing classifier for skin-infection detection

A method of constructing a classifier for skin-infection detection comprising:

• constructing a codebook of representative features based on a plurality of target-disease-irrelevant images;
• extracting a plurality of transfer-learned target-disease features from a plurality of target-disease images according to the codebook, wherein the step of extracting the plurality of transfer-learned target-disease features comprises:
• segmenting a target region from each of the target-disease images to correspondingly generate a segmented target-disease image;
• and
• extracting the transfer-learned target-disease features from the segmented target-disease images;
• and
• performing supervised learning based on the transfer-learned target-disease features to train the classifier for skin-infection detection,
• wherein the step of segmenting the target region from each of the target-disease images to correspondingly generate the segmented target-disease image comprises:
• for each of the target-disease images:
• initializing a contour at a center of the target region according to color;
• evolving the contour such that a predefined energy function is minimized so as to obtain a terminated boundary;
• and
• segmenting the target region from the target-disease image according to the terminated boundary to generate the segmented target-disease image.

An electronic apparatus comprising:

• a communication interface;
• a memory, recording a plurality of modules;
• and
• one or a plurality of processors, coupled to the communication interface and the memory, and accessing and executing the modules stored in the memory, wherein the modules comprise:
• a codebook constructing module, constructing a codebook of representative features based on a plurality of target-disease-irrelevant images obtained via the communication interface;
• a feature extracting module, extracting a plurality of transfer-learned target-disease features from a plurality of target-disease images obtained via the communication interface according to the codebook, wherein the feature extracting module segments a target region from each of the target-disease images to correspondingly generate a segmented target-disease image, and extracts the transfer-learned target-disease features from the segmented target-disease images;
• and
• a classifier training module, performing supervised learning based on the transfer-learned target-disease features to train the classifier for skin-infection detection,
• wherein for each of the target-disease images, the feature extracting module initializes a contour at a center of the target region according to color, evolves the contour such that the energy function is minimized so as to obtain a terminated boundary, and segments the target region from the target-disease image according to the terminated boundary to generate the segmented target-disease image.

A non-transitory computer readable medium, storing programs to be loaded into an electronic apparatus to perform steps of:

• constructing a codebook of representative features based on a plurality of target-disease-irrelevant images;
• extracting a plurality of transfer-learned target-disease features from a plurality of target-disease images according to the codebook, wherein the step of extracting the plurality of transfer-learned target-disease features comprises:
• segmenting a target region from each of the target-disease images to correspondingly generate a segmented target-disease image;
• and
• extracting the transfer-learned target-disease features from the segmented target-disease images;
• performing supervised learning based on the transfer-learned target-disease features to train the classifier for skin-infection detection,
• wherein the step of segmenting the target region from each of the target-disease images to correspondingly generate the segmented target-disease image comprises:
• for each of the target-disease images:
• initializing a contour at a center of the target region according to color;
• evolving the contour such that a predefined energy function is minimized so as to obtain a terminated boundary;
• and
• segmenting the target region from the target-disease image according to the terminated boundary to generate the segmented target-disease image.