RegionProps

I want to select the pieces of an image that have these caracteristics: area > 10 and Eccentricity > 0,4.

How can i combine those in one fuction?

Thank you for the attention.

Daniel Carvalho

[John Wick]

I = imread('cell.tif'); [~,threshold] = edge(I,'sobel'); fudgeFactor = 0.5; BWs = edge(I,'sobel',threshold * fudgeFactor); se90 = strel('line',3,90); se0 = strel('line',3,0); BWsdil = imdilate(BWs,[se90 se0]); BWdfill = imfill(BWsdil,'holes'); BWnobord = imclearborder(BWdfill,4); seD = strel('diamond',1); BWfinal = imerode(BWnobord,seD); BWfinal = imerode(BWfinal,seD); subplot(3, 3, 1), imshow(I),title('Orginal image'); subplot(3, 3, 2), imshow(BWs),title('Binary Gradient Mask'); subplot(3, 3, 3), imshow(BWsdil),title('Dilated Gradient Mask'); subplot(3, 3, 4), imshow(BWdfill),title('BI with Filled Holes'); subplot(3, 3, 5), imshow(BWnobord),title('Cleared Border Image'); subplot(3, 3, 6), imshow(BWfinal),title('Segmented Image'); subplot(3, 3, 7), imshow(labeloverlay(I,BWfinal)),title('Mask Over Image');

Answer 1

[Sean de Wolski]

I = rand(100)>.75; %sample binary image
CC = bwconncomp(I);  %Connected Components Analysis
stats = regionprops(CC,'area','eccentricity');  %regionprops

idx = [stats(:).Area]>10&[stats.Eccentricity]<0.4; 
%logical indices corresponding to area > 10 and eccentricity <0.4.

Do what you what you want with CC,idx; all of the information is there.

[Daniel]

thank you sean.

the image that is obtained is just a line. Do you know how to solve this?

[Sean de Wolski]

What do you mean the image obtained is just a line?

Is that the only object with 10px connected and an eccentricity <0.4?

Show us the code!

[Daniel]

sorry about the delay but i wasn't able to come here sooner. it was a noob error that i was doing. thank you very much

[John Wick]

g1 = imread("C:\Users\19b-140-CS\Desktop\lab08\skeleton.png"); img1 = im2gray(img1); lap = fspecial('laplacian'); fltimg = imfilter(img1,lap); enhanced_img = img1 - fltimg; sob = fspecial('sobel'); sobell = imfilter(enhanced_img, sob); filterSize = 5; filter = ones(filterSize) / (filterSize^2); smoothedImage = imfilter(sobell, filter); %sharpen = imsharpen(smoothedImage); image1 = im2double(enhanced_img); image2 = im2double(smoothedImage); lowPassFilter = fspecial('average', filterSize); highFreqImage = enhanced_img - imfilter(enhanced_img, lowPassFilter); lowFreqImage = imfilter(smoothedImage, lowPassFilter); sharpImage = highFreqImage + lowFreqImage; power_img = 1 * im2double(sharpImage(:,:)).^ (0.8); subplot(2, 3, 1), imshow(img1),title('Orginal image'); subplot(2, 3, 2), imshow(fltimg),title('Lap Flt Image'); subplot(2, 3, 3), imshow(enhanced_img),title('Enhanced Image'); subplot(2, 3, 4), imshow(sobell),title('Sobel Flt Image'); subplot(2, 3, 5), imshow(smoothedImage),title('smoothed Image'); subplot(2, 3, 6), imshow(power_img),title('Sharp Imag