A brief discussion of some of the segmentation methods is described below.
Canny edge detection:
The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. John F. developed method uses a filter based on the derivative of a Gaussian to compute the intensity of the gradients. Canny edge detection is a multi-step algorithm that can detect edges with noise suppressed at the same time. Smooth the image with a Gaussian filter to reduce noise and unwanted details and textures.
Advantages:
- The presence of a Gaussian filter allows removing of any noise in an image.
- The signal can be enhanced concerning the noise ratio by the non-maxima suppression method, which results in one pixel wide ridges as the output.
- Detects the edges in a noisy state by applying the thresholding method.
- The effectiveness can be adjusted by using parameters.
- It gives a good localization, response and is immune to a noisy environment.
Disadvantages:
- The primary disadvantage of using the Canny edge detector is that it consumes a lot of time due to its complex computation.
- It is difficult to implement to reach the real-time response.
MORPHOLOGICAL SEGMENTATION:
Morphological Segmentation is an ImageJ plugin that combines morphological operations, such as extended minima and morphological gradient, with watershed flooding algorithms to segment grayscale images of any type in 2D and 3D.
The purpose of morphological segmentation is to decompose words into smaller units, known as morphemes, which are typically taken to be the smallest meaning-bearing units in a language. This work concerns itself with modelling hierarchical structure over these morphemes.
Morphological Operations:
Dilate, erosion, reconstruct, perform other morphological operations. Morphology is a broad set of image processing operations that process images based on shapes. In a morphological operation, each pixel in the image is adjusted based on the value of other pixels in its neighbourhood.
Erosion (usually represented by ⊖):
It is one of two fundamental operations (the other being dilation) in morphological image processing from which all other morphological operations are based. It was originally defined for binary images, later being extended to grayscale images, and subsequently to complete lattices.
Dilated Convolution:
In simple terms, dilated convolution is just a convolution applied to input with defined gaps. With these definitions, given our input is a 2D image, dilation rate k=1 is normal convolution, and k=2 means skipping one pixel per input and k=4 means skipping 3 pixels.
THRESHOLD SEGMENTATION:-
The thresholdingThe thresholding technique can be used for MRI brain tumourtumour segmentation. According to
intensity/brightness is a simple technique for images that contain solid objects on a background of different but uniform, brightness. Each pixel is compared to the threshold if its value is higher than the threshold, the pixel is considered to be “foreground” and is set to white, and if it is less than or equal to the threshold it is considered “background” and set to black. Various versions of thresholding technique have been introduced that segments MR images by using the information based on local intensities and connectivity. In most of the existing thresholding methods are bi-level, which use two levels to categorize the image into background and object segments. However, MR images have many different parts which make these methods non-applicable. Thus, the loss of information from the image may occur and diagnoS1s systemic may mislead physicians in their clinical task. Therefore, multi-level thresholding algorithms have been developed to ensure that all important information from MR images are retained, but they become computationally expensive, because a large no. of iterations would be required for computing the optimum threshold. Otsu’s global thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image. It selects that gray level value as threshold for which between-class variance is maximized. In general, thresholding algorithms do not use spatial information of an image and they usually fail to segment objects with low contrast or noisy images with varying background. The simplest method of image segmentation is called the thresholding method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image. The key of this method is to select the threshold value (or values when multiple levels are selected). A zero matrix of same size of original image matrix is considered. Each pixel value of the image matrix is compared with the threshold point. If the value of pixel 18 greater than threshold, coordinate of matrix is assigned a value 255 otherwise 0 is assigned to that. This process is repeated till all the pixel values are compared to threshold point. A grayscale image is termed into a binary (black and white) image by first choosing a grey level T in the original image, and then turning every pixel black or white according to whether its
grey value is greater than or less than T:
A pixel becomes, white if its grey level is T,
black if its grey level is T.
Matrix is then transformed into image using ‘imshow command.