A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Detail-preserving median based filters in image processing
Pattern Recognition Letters
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
An edge detection technique using local smoothing and statistical hypothesis testing
Pattern Recognition Letters
Optimal weighted median filtering under structural constraints
IEEE Transactions on Signal Processing
A computational algorithm for minimizing total variation in image restoration
IEEE Transactions on Image Processing
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
A local spectral inversion of a linearized TV model for denoising and deblurring
IEEE Transactions on Image Processing
Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images
IEEE Transactions on Image Processing
Thresholding in edge detection: a statistical approach
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing in advanced nondestructive materials inspection
Hi-index | 0.10 |
To extract morphological features about nuclei from microscopy cellular image, it is usually required to find the edges of nuclei at first. Standard edge detection methods may not produce satisfactory results due to the varying brightness and background in cellular image. It is important to extract close, smooth, and correct edges in order to compute features like compactness, convexity, roundness, and etc. We present a new method to detect edges of nuclei in microscopy images. The method is based on using median filtering to compute the total variation with respect to the central pixel in a filter window. This step exploits one important feature of median filter, i.e., within the filter window, the total variation (TV) with respect to the median is always less than or equal to the TV with respect to the original center pixel. In other words, median filtering looks for the output that minimizes the total variation within the filtering window. The resulting image has enhanced contrast along the boundary of nuclei. As the final step, we use popular edge detection methods such as Canny detector and Laplacian of Gaussian to find edges of nuclei in the image. Examples from processing real cellular image obtained by light microscope show that the method obtains better edges in terms of connectivity, smoothness, and closely following the boundary of nuclei.