System and method for counting follicular units

Abstract

A system and method for counting follicular units using an automated system comprises (i) acquiring a digital image of a body surface having skin and a plurality of follicular units; (ii) selecting a region of interest with the digital image; (iii) segmenting the selected image to produce a binary image; (iv) performing a morphological open operation on the binary image; and (v) performing noise filtering by removing objects having certain characteristics which do not correspond to hair.

1 . A method of identifying follicular units on a body surface having skin and hair, comprising:
 acquiring a digital image of a body surface;   selecting a region of interest within said digital image called a selected image;   digital filtering said selected image, via a band-pass filter, to remove components corresponding to the skin;   segmenting said selected image to produce a binary image;   performing morphological open operation on said binary image; and   performing noise filtering by removing objects having certain characteristics which do not correspond to such characteristics of hair.   
   
   
       2 . The method of  claim 1 , wherein said step of filtering said selected image via a band-pass filter comprises low-pass filtering the selected image twice and then subtracting the two resulting filtered images. 
   
   
       3 . The method of  claim 1 , wherein said step of filtering said selected image via a band-pass filter comprises a first filter step in which said selected image is filtered using a low-pass filter having a first kernel and a second filter step in which said selected image is filtered using a low-pass filter having a second kernel. 
   
   
       4 . The method of  claim 3 , wherein said first kernel is a Gaussian kernel having substantially the following characteristics, support 21 pixels, sigma of 1.0. 
   
   
       5 . The method of  claim 3 , wherein said second kernel is a Gaussian kernel having substantially the following characteristics, support 21 pixels, sigma of 0.75. 
   
   
       6 . The method of  claim 3  wherein said first kernel is a Gaussian kernel having substantially the following characteristics, support 21 pixels, sigma of 1.0 and said second kernel is a Gaussian kernel having substantially the following characteristics, support 21 pixels, sigma of 0.75. 
   
   
       7 . The method of  claim 1 , further comprising the following steps:
 acquiring a second digital image in stereo correspondence to said first digital image;   computing the coordinate position of a hair using said first and second digital images; and   filtering out images having a computed coordinate position which is inconsistent with a hair on said body surface.   
   
   
       8 . The method of  claim 1 , further comprising counting the discrete images of hairs using said processed image. 
   
   
       9 . The method of  claim 1 , wherein said step of noise filtering comprises filtering out any object having an area that differs by more than two standard deviations from the mean object size within the selected image. 
   
   
       10 . The method of  claim 1 , wherein said step of noise filtering comprises filtering out any image with an area that is larger than two standard deviations larger than the mean object size. 
   
   
       11 . The method of  claim 1 , wherein said characteristics include one or more of area, location and orientation. 
   
   
       12 . The method of  claim 1 , wherein said step of noise filtering comprises filtering out objects whose characteristics do not correspond to such characteristics for the other objects in the region of interest. 
   
   
       13 . The method of  claim 1 , wherein said step of noise filtering comprises filtering out objects whose characteristics do not correspond to such characteristics for hair based on a sampling of hairs on the body surface. 
   
   
       14 . The method of  claim 1 , wherein said step of noise filtering comprises filtering out objects whose characteristics do not correspond to such characteristics expected for hairs based on predetermined data. 
   
   
       15 . The method of  claim 3  wherein said first kernel is different from said second kernel.