It's [left, top, width, height]. Just be aware that the left and top are 0.5 pixels to the left and above, respectively, than the actual first column and first row of the binary image. It's because the Bounding box is defined such that it "contains" the blob, and if it ran right through the center of the top-most and left-most pixel, then that definition becomes somewhat ambiguous.

Splitting image in small parts would be the obvious choice but it could split a connected region in 2 or more subimages Any method that involves splitting the image into sub-images and processing the sub-images, runs the risk that you might miss connectivity that extends over sub-image boundaries. blockedImage and apply and the older blockproc pass in partial images and assume the processing on the partial images can be handled independently. They do not transform the called function to be able to handle large arrays without running out of memory, and they have no idea how to synthesize the results of processing the sub-images together to form a complete result. The sort of thing you would need would be to use regionprops with a tall array. Unfortunately, regionprops does not support tall arrays. You are going to have to split the image up into sub-images, process that, get the results out, and then patch up the boundaries -- such as checking to see whether any connected components extend to the sub-image boundaries in adjacent sub-images, and if so then adjusting the information, such as re-labeling. bw = bwconncomp(bw); I am morally certain that is not going to return "correct" information when you use blocked images. because the function should return a blocked image and not a table. If you use the older blockproc() then it is not required that the output be the same size as the input block -- but if you use overlapping blocks, then make sure you use options to not trim the output. You have to return something numeric, but as long as you can make the output a consistent size then you could potentially pack data into a numeric array. This would not work very well for returning per-region information (unless you set a fixed maximum number to return), but it would work, for example, to return information about the largest region.

Try this for a start: img = zeros(5,5); img(2:4, 2:4) = 1; props = regionprops(img, 'PixelList'); disp(img); disp(props.PixelList); You can change the pattern of ones in img until you're clear about how the result is structured. Another simple but informative example: img = zeros(5,5); img(2,2) = 1; img(4,4) = 2; props = regionprops(img, 'PixelList'); disp(props(1).PixelList); disp(props(2).PixelList);