Skin lesion segmentation using deep convolution networks guided by local unsupervised learning

Patent metadata

Field	Value
Publication number	US-10229499-B2
Application number	US-201715858191-A
Country	US
Kind code	B2
Filing date	Dec 29, 2017
Priority date	Aug 31, 2016
Publication date	Mar 12, 2019
Grant date	Mar 12, 2019

Abstract

A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score. Constructing a super-pixel graph G=(V,E,W) wherein
w ij = exp ( -  x i - x j  2 σ ) ⁢ ⁢ and ⁢ ⁢ d i = ∑ i = 1 N ⁢ w ij ;
computing a confidence score function F according to {circumflex over (F)}=arg min(F T LF+μ∥F−Y∥ 2 ); and integrating the confidence score function F with the pixelwise prediction scores to produce a final segmentation of the dermoscopic image into lesion and background areas.

First claim

What is claimed is:

1. A method comprising:
obtaining a dermoscopic image;
running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise prediction scores for presence or absence of a lesion;
segmenting the dermoscopic image into a plurality of super-pixels;
computing for each super-pixel a prediction score as the average of the pixelwise prediction scores for all pixels within that super-pixel;
computing a mean prediction score across the plurality of super-pixels;
constructing an indicator vector Y in which a confidence indicator of “1” is assigned to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” is assigned to each super-pixel with a prediction score less than the mean prediction score;
constructing a super-pixel graph G=(V,E,W) wherein

w
ij

=

exp
(

-



x
i

-

x
j



2

σ

)

⁢

⁢
and
⁢

⁢

d
i

=

∑

i
=
1

N

⁢

w
ij

,

 wherein V is a matrix of vertices representing the super-pixels, E is a matrix of edges representing similarities among the super-pixels, W is a matrix of weights of edges representing degrees of similarity among the super-pixels, w ij are elements of W, x i and x j are individual super-pixels, σ is a constant that scales the strength of each element of W, and d i is a degree of super-pixel x i;
computing a confidence score function F according to {circumflex over (F)}=arg min(F T LF +μ∥F −Y∥ 2 ), wherein L is the Laplacian of F and {circumflex over (F)}=(D −λW) −1 Y, wherein D is a matrix of d i , wherein λ=1/1+μ and μ=0.01; and
integrating the confidence score function F with the pixelwise prediction scores to produce a final segmentation of the dermoscopic image into lesion and background areas.

2. The method of claim 1 wherein the confidence score function F is integrated with the pixelwise prediction scores as the vector of image pixels confidence scores.

3. The method of claim 1 wherein the final segmentation has a lesion area where at least two adjacent pixels have prediction scores >0.5 and has a background area where at least two adjacent pixels have prediction scores <=0.5.

4. The method of claim 1 further comprising augmenting the dermoscopic image by rotating a lesion area of the dermoscopic image at least once by .pi./4 radians.

5. The method of claim 4 further comprising augmenting the dermoscopic image by rotating the lesion area at least four times by .pi./4 radians.

6. The method of claim 1 wherein the convolutional neural network is based on the Oxford Visual Geometry Group network.

Assignees

• IBM

Inventors

• Bozorgtabar Seyedbehzad
• Garnavi Rahil
• Roy Pallab
• Sedai Suman

Classifications

• G06V10/82

  using neural networks · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/2411

  based on the proximity to a decision surface, e.g. support vector machines · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title