The abnormal growth of cells in the liver is known as liver cancer or hepatic cancer. There are various image processing techniques involving Ni-LabView to detect and analyse the region affected by cancer in the liver. Here, the tumor is detected from computed tomography images. This project involves the detection, segmentation and analysis of cancer in the liver. This method will improve the diagnosis of liver cancer leading to earlier treatment and to a better quality of life for liver cancer patients.
2.1 AUTOMATIC THRESHOLD TECHNIQUES
When compared to manual thresholding, the automatic thresholding techniques does not require the lower and upper threshold level setting. Based on literatures, the automatic thresholding techniques are well applicable for the images which vary in light intensity. The best multi-class thresholding method is clustering technique, which results in creating tertiary or high-level images by operating on multiples classes. The other methods like entropy, moments, interclass and metric are to be used on binary thresholding techniques. The main criteria in image processing is choosing the right algorithm to apply on an image; this strictly depends upon the type of image. Prior to the implementation of automatic thresholding on the source image, the image may be inverted in grayscale to obtain better results. So, inverting the image in terms of grayscale can be performed on source images where the background is brighter than foreground.
Image segmentation is an important stage in processing the source image. Successful segmentation are achieved through clustering method. The clustering technique will segment or partition the given source dataset into number of groups, namely clusters. Certain literatures have proposed various clustering techniques like K-means algorithm, Improved K-means, Fuzzy C-mean and Improved Fuzzy C-mean algorithm. Each algorithm varies from one another in certain characteristics like the number of iterations, complexity, computational efficiency, etc. The K-means clustering method is majorly used due to its simple implementations and good computational efficiency; whereas the Improved K-means clustering method brings an advantage in using them by reducing the number of iterations performed. The Fuzzy C-means abbreviated as FCM and Improved FCM vary by their implementation time i.e. FCM method is time consuming and it is overcome by Improved FCM. Additional advantage of FCM method is providing good flexibility to the pixels, to belong to n-number of classes with varying degrees of membership. Based on the experimental results, Improved FCM results in better segmentation compared to other clustering techniques, proved by providing great accuracy on segmenting input gray scale image. It is important to measure the quality of output image i.e. segmented image through certain statistical parameters like RI – Rand Index, GCE – Global Consistency Error, VOI – Variations of Information and BDE – Boundary Displacement Error.
Histogram of an image is necessary to determine the average information or entropy present in the source image. Histogram is a bar chart like representation displaying all the probabilities of grey levels present in the given image. The entropy is useful, for example, for automatic image focusing: as the state of focusing of an image varies, so does its entropy is shown in Fig. 2. We propose a method with good accuracy and lower processing time to measure the entropy of image. In the proposed method, a simple approximation formula is derived to find the entropy of images. An existing method is utilized here to perform the analogue calculation of the proposed formula at TV speed, achieved through TV optical non-linear component. This ensures the usage of proposed method for focussing 2D or 3D images in real time TV system.
2.4 LOCAL THRESHOLDING
Binary images are created by segmenting the input grayscale image into two regions namely, the particle region and background image through a process called local adaptive thresholding. The global grayscale thresholding method, generally segments the image based on the pixels; it assumes the pixel comes under either particle region or background region taking into account the intensity statistics of the entire image; whereas the local thresholding utilizes the intensity statistics of neighbouring pixels.
2.4.1 NIBLACK ALGORITHM
The NiBlack algorithm is one among the other existing adaptive thresholding algorithms, concluded from OCR and map image segmentation applications. This method is widely accepted for display inspection and OCR, image thresholding applications. The window size has to be carefully designed as the algorithm is sensitive to it, thereby producing noisy image segmentation of image with large and uniform background. To categorize pixels properly, deviation factor is computed using NI Vision local thresholding.
The NiBlack algorithm is given as,
T (i, j) = m (i, j) + k.ω (i, j)
Where, T (i, j) – local threshold value at pixel (i, j)
m (i, j) – local sample mean
k – deviation factor
𝝎 (i, j) – standard deviation
2.4.2 BACKGROUND CORRECTION ALGORITHM
The background correction algorithm has combined the concepts of local thresholding and global thresholding concepts. The figure 3 demonstrates the background correction algorithm.
The background-corrected image is given by,
B (i, j) = I (i, j) – m (i, j),
Where, m (i, j) is the local mean at pixel (i, j).