Cell Segmentation in Slide Images

-
Cell Segmentation in Slide Images


Output

OVERVIEW

The advent of whole slide images (i.e. digitally scanning pathology slides can help usher in a new era of quantitative analysis of tissue samples. Whereas traditional judgments are made subjectively by a
pathologist, image processing and computer vision algorithms can help determine cell densities in
neoplasmic tissues, quantify the amount of Her2 in breast cancer, and much more.

One of the largest challenges in digital pathology is accurate cell segmentation. In tissue samples, cells are closely packed, and sometimes overlap since the tissue being imaged is actually a 3D specimen, of which we are taking a 2D image. We propose to develop an image processing algorithm for segmenting distinct cells in tissue samples. A number of algorithms have already been proposed for this, including Euclidean distance transform, watershed segmentation, a combination of morphology operators, Laplacian of Gaussian filters, MSERdetectors, and Hough transforms.We will experiment with up to three of these different algorithms, compare the results for a set of 10
images from the Iowa State Virtual Slidebox, which is a large database of histopathology slides. Then,we will analyze why they did or did not perform well.

IEEE Rev Biomed Eng. 2016;9:234-63. doi: 10.1109/RBME.2016.2515127. Epub 2016 Jan 6. Robust
Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive
Review [Link].
R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,”
Commun. ACM, vol. 15, no. 1, pp. 11–15, Jan. 1972.
J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol.PAMI-8, no. 6, pp. 679–698, Nov. 1986.

FOR BASE PAPER PLEASE MAIL US

DOWNLOAD PROJECT SOURCE CODE CLICK HERE

Comments

Post a Comment

Popular posts from this blog

Light Field Images for Background Removal

-
Image
Light Field Images for Background Removal OUTPUT OVERVIEW Standard edge detection or foreground/background separation techniques, such as Otzu’smethod, require color or intensity differences between the background and regions that need tobe separated. For example, green screens are routinely set up as the background in a scene so thatthere is a clear difference in color between the background and foreground.Light filed images, captured in 4D and passively containing depth information for thescene can be used to approximate this effect. Depth estimation in the scene alone could provide a metric to separate the foreground and background, but more sophisticated methods are available.Considering the edge detection from a single image from a single viewpoint and analyzing the depths from the light field around the edges in that image, occluded edges and intensity or color edges can be distinguished. Along with the rest of the depth information this can allow the foreground and b
Read more

Using Image Processing to Identify and Score Darts thrown into a Dartboard

-
Image
✔️Using Image Processing to Identify and Score Darts thrown into a Dartboard ✔️Introduction The game of darts is a throwing sport in which participants toss projectiles into a circular target  attached to a vertical surface. The target is divided into many regions which correspond to different point and multiplier values. Darts is commonly played across North America and Europe and is a popular past time for many so an application that allows players to more conveniently keep score would be widely  beneficial. “Darts” is a general term for a targeting game following this basic premise and many game variations exist within this archetype; all utilizing the standard dart projectiles with a regulation dart board. Each game variant may have different objectives for which players to aim but identifying the region in  which the dart has hit in the dartboard is necessary for proper scoring. This project proposes the use of image processing to identify thrown darts and to det
Read more

Digital Make up Face Generation

-
Image
Digital Make up Face Generation Goal Current make-up applications rely on using photoshop tools to apply makeup on the target's digital  faces and generate results. While these applications allow customization, a customer who wants to  quickly decide on the type of makeup kit to buy at a store will not find it useful. The customer might just  want to find out how the make-up look on the cover or billboard will look like on her face. The goal of  this project is to use an existing reference image of another subject with a make-up applied, and  transfer the reference's make up on the target's face. The application can be further extended to photo  retouching and illumination transfer from the reference image to the target. Methodology In order to transfer the make-up from the reference onto target in a pixel by pixel basis, the areas of  interest must align. Face features such as eyes, nose, mouth and contours of the face, will be recognized  using Active Shape
Read more