# US 9,750,450 B2 · Method, electronic apparatus, and computer readable medium of constructing classifier for skin-infection detection

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 9,750,450 B2
- Document ID: US9750450B2
- Status: Granted
- Kind: B2
- Application number: 14/857,820
- Filed: 2015-09-18
- Published: 2017-03-23
- Granted: 2017-09-05
- Earliest claimed: 2015-09-18
- Assignees: HTC Corporation
- Applicants: HTC Corporation
- Inventors: Chuen-Kai Shie; Chung-Hsiang Chuang; Chun-Nan Chou; Meng-Hsi Wu; Edward Chang
- Web page: https://patentgrep.com/patent/US9750450B2

## CPC

- A61B 5/444: {Evaluating skin marks, e.g. mole, nevi, tumour, scar}
- A61B 5/7267: {involving training the classification device}
- G06F 18/2413: based on distances to training or reference patterns
- G06F 18/2431: Multiple classes
- G06F 18/28: Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/0014: {using an image reference approach}
- G06T 7/12: Edge-based segmentation
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/772: Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries
- G06V 10/82: using neural networks
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06T 2207/10004: Still image; Photographic image
- G06T 2207/20076: Probabilistic image processing
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/20112: Image segmentation details
- G06T 2207/30088: Skin; Dermal
- G06T 2207/30096: Tumor; Lesion
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

The disclosure provides a method, an electronic apparatus, and a computer readable medium of constructing a classifier for skin-infection detection. The method includes the following steps. A codebook of representative features is constructed based on a plurality of target-disease-irrelevant images. Transfer-learned disease features are extracted from target-disease images according to the codebook without any medical domain knowledge, where the target-disease images are captured by at least one image capturing device. Supervised learning is performed based on the transfer-learned target-disease features to train the classifier for skin-infection detection.

## Claims

### Claim 1

A method of constructing a classifier for melanoma 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 melanoma 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.

### Claim 2

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.

### Claim 3 (depends on 2)

The method according to claim 2, wherein the step of constructing the codebook of the representative features based on the target-disease-irrelevant images comprises:
constructing the codebook of the representative features based on the target-disease-irrelevant images by using a first representation learning model, wherein the first representation learning model comprises a plurality of layers of neurons, wherein the layers comprise at least one inner layer, and wherein activation values of the inner layers corresponds to the representative features of the target-disease-irrelevant images.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the step of extracting the transfer-learned target-disease features from the target-disease images according to the codebook comprises:
for each of the target-disease images:
inputting the target-disease image into the first representation learning model;
propagating information in the target-disease image through the layers; and
obtaining the transfer-learned features of the target-disease image according to at least one of the inner layers.

### Claim 5 (depends on 2)

The method according to claim 2, wherein the step of constructing the codebook of the representative features based on the target-disease-irrelevant images comprises:
constructing the codebook of the representative features based on the target-disease-irrelevant images by using a second representation learning model, wherein the second representation learning model comprises a plurality of layers of units, wherein the layers comprise at least one hidden layer having a plurality of hidden units, and wherein probabilistic values of the hidden units correspond to the representative features of the target-disease-irrelevant images.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the step of extracting the transfer-learned features from the target-disease images according to the codebook comprises:
for each of the target-disease images:
inputting the target-disease image into the second representation learning model;
propagating information in the target-disease image through the layers; and
obtaining the transfer-learned features of the target-disease image according to at least one of the hidden units.

### Claim 7 (depends on 2)

The method according to claim 2, wherein after the step of performing supervised learning on at least the transfer-learned features to train the classifier for skin-infection detection, the method further comprises:
capturing a new image by an image capturing device; and
classifying whether the new image corresponds to a skin infection condition by using the classifier for skin-infection detection to generate a classification result; and
outputting the classification result.

### Claim 8

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.

### Claim 9 (depends on 8)

The electronic apparatus according to claim 8, wherein the codebook constructing module constructs the codebook based on the target-disease-irrelevant images by using a first representation learning model, wherein the first representation learning model comprises a plurality of layers of neurons, wherein the layers comprise at least one inner layer, and wherein activation values of the inner layers corresponds to the representative features of the target-disease-irrelevant images.

### Claim 10 (depends on 9)

The electronic apparatus according to claim 9, wherein for each of the target-disease images, the feature extracting module inputs the target-disease image into the first representation learning model, propagates information in the target-disease image through the layers, and obtains the transfer-learned features of the target-disease image according to at least one of the inner layers.

### Claim 11 (depends on 9)

The electronic apparatus according to claim 9, wherein the classifier obtains a new image captured by an image capturing device via the communication interface, classifies whether the new image corresponds to a skin infection condition to generate a classification result, and outputs the classification result.

### Claim 12 (depends on 8)

The electronic apparatus according to claim 8, wherein the codebook constructing module constructs the codebook based on the target-disease-irrelevant images by using a second representation learning model, wherein the second representation learning model comprises a plurality of layers of units, wherein the layers comprise at least one hidden layer having a plurality of hidden units, and wherein probabilistic values of the hidden units correspond to the representative features of the target-disease-irrelevant images.

### Claim 13 (depends on 12)

The electronic apparatus according to claim 12, wherein for each of the target-disease images, the feature extracting module inputs the target-disease image into the second representation learning model, propagates information in the target-disease image through the layers, and obtains the transfer-learned features of the target-disease image according to at least one of the hidden units.

### Claim 14

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.