Digital Image Processing Using MATLAB Z2H

DFT (Discrete Fourier Transform) Analysis Digital Image Processing Using MATLAB Fourier Transform The Fourier Transform is a significant image processing tool which is used to decompose an image into its sine and cosine components. The output of the conversion represents the image in the Fourier or frequency domain, though the input image is the spatial domain correspondent. Fourier domain image, each point signifies a particular frequency contained in the spatial domain image. The Fourier Transform is used in a wide variety of applications, such as image filtering, image rebuilding, image compression and Image investigation Periodic Signals A continuous-time signal x(t) is periodic if: x(t + T) = x(t) Fundamental period,             T0, of x(t) is smallest T satisfying  above equation. Fundamental frequency:             f0 = 1/T0 Fundamental angular frequency:             ω0 = 2π/T0 = 2πf0 Periodicity in Biology and Medicine Fourier analysis Fourier series Expansion o

Different types of Color Image Processing Using MATLAB with Source Code Digital Image Processing Using MATLAB Why use color in image processing? Color is a powerful descriptor. Object identification and extraction Example: Face detection using skin colors Humans can discern thousands of color shades and intensities Human discern only two dozen shades of grays Types of color image processing Pseudo-color processing Full color processing Full color processing Images are acquired from full-color sensor or equipment Pseudo-color processing In the past period, color sensors and processing hardware are not available Colors are assigned to a range of monochrome intensities Color fundamentals Color models Pseudo-color image processing Color transformations Smoothing and sharpening COLOR FUNDAMENTALS Physical phenomenon Physical nature of color is identified Psysio-psychological phenomenon How human brain see and understand color? Visible light Chromatic light distanc

Image Enhancement Technique in Frequency Filter using MATLAB with Source Code Digital Image Processing Using MATLAB Enhancement in Frequency Domain Filtering Spatial Domain Frequency Domain Major filter categories Naturally, filters are categorized by examining their properties in the frequency domain: Low-pass High-pass Band-pass Band-stop Example Low-pass filters (Smoothing Filters): Low-pass filters also known as called Smoothing Filters Preserve low frequencies - useful for noise suppression Example: High-pass filters (Sharpening Filters) High-pass filters also known as called Sharpening Filters Preserves high frequencies - useful for edge detection Example Band-Stop filters Frequency Domain Methods Case 1:  h(u,v) is stated in the frequency domain. Case 2:  h(x,y) is stated in the spatial domain. Frequency domain filtering: STEPS F(u,v) = R(u,v) + jI(u,v) G(u,v)= F(u,v)H(u,v) = H(u,v) R(u,v) + jH(u,v)I(u,v) Types of Low Pass (LP) Filters Ideal low-pas

Image Enhancement Technique in Spatial Filtering Method using MATLAB with Source Code Digital Image Processing Using MATLAB Introduction Filter term in “Digital image processing” is stated to the subimage, filtering is a technique for adapting or enhancing an image. There are others term to call subimage such as kernel, mask, template, or window The value in a filter subimage are stated as coefficients, rather than pixels. Basics of Spatial Filtering Spatial filtering term is the filtering operations that are performed directly on the pixels of an image, Spatial domain operation or filtering (the processed value for the present pixel processed value for the present pixel be contingent on both itself and nearby pixels). Henceforth Filtering is a region operation, the value of any assumed pixel in the output image is determined by applying some procedure to the values of the pixels in the neighborhood of the consistent input pixel. A pixel's area is some set of pixels, define

Spatial Intensity Resolution of an image Using MATLAB with Source Code INTRODUCTION Sampling is the principal factor Estimation of the spatial resolution of an image. Commonly spatial resolution is the smallest perceptible detail in an image, a widely used meaning of resolution is simply the smallest number of discernible line pairs per unit distance ; for estimation 100 line pairs/mm. Gray level resolution: This refers to the smallest visible change in gray level. The measurement of visible changes in gray level is a extremely subjective procedure. We have significant discretion concerning the number of Samples used to generate a digital image. But this is not true for the amount of gray levels. Due to hardware restraints, the number of gray levels is usually an integer power of two. The most common value is 8 bits. It can vary depending on application. When an actual portion of physical resolution relating pixels and level of detail they resolution in the original scene are not n

Histogram Equalization Introduction: This application note defines a technique of Digital imaging processing which allows medical images to have better contrast. This is achieved via the histogram of the image, making use of a technique that allows the places with minimal contrast to improvement increasing contrast by circulation out the most regular intensity values. For example, in digital x-rays in what kind of colors achieved are a palette of whites and blacks, unlike types of colors give the physician an idea of the type of density that he or she is perceiving. Therefore white components are most likely to indicate bone or water and black structures mean air. Whenever pathologies are existing in an image, attempting to define the region of the lesion or object of interest may be a challenge, because a variety of structures are usually layered one over the other. like in the case of the chest the heart, lungs, and blood vessels are so near with each other that contrast is critic

Intensity Transformation Using MATLAB Theoretical Concepts: Introduction What is Intensity Transformation? Intensity transformation is increase the contrast between certain Intensity values, most important application of intensity transformation is Enhance the low Quality image. Image Enhancement Techniques Spatial operates on Pixels Frequency domain operates on Fourier transform of image Spatial Domain Methods Spatial Domain Technique a operation (linear or non-linear) is performed on the pixels in the neighborhood of coordinate (x,y) in the input image F, giving enhanced image F’ Neighborhood can be any shape but generally it is rectangular ( 3x3, 5x5, 9x9) g(x,y) = T[f(x,y)] Grey Scale Manipulation Simplest form of window (1x1) Assume input gray scale values are in range [0, L-1] (in 8 bit images L = 256) Nth root Transformation S = c (r)n . Intensity Transformation function Linear: Negative Identity Logarithmic:  Log Inverse Log Power-Law:  nth power, nth

Image segmentation Using MATLAB Digital Images Processing Using MATLAB What is segmentation? Image segmentation is the process of partitioning an image into a collection of connected sets of pixels. The goal of segmentation is to simplify change the representation of an image into something that is more meaningful and easier to analyses. Image segmentation is typically used to locate objects and boundaries like lines, curves in images. It is the process of assigning a label to every pixel in an image.  Three main techniques to do. Thresholding Region based segmentation Edge based segmentation Thresholding Thresholding is finding histogram of gray level intensity. Types Basic Global Thresholding Multiple Threshold Variable Thresholding Otsu’s Technique Basic Global Thresholding Primarily Segment image use: Calculate the average intensity m1 and m2 for the pixels Calculate a fresh threshold:        Until the variance betwe

Basic Morphological Operation Using  MATLAB Digital Image Processing using MATLAB Introduction I mage Processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. Image Morphology is an important tool in image processing. It is the study of shapes of object present in the image and extraction of image features. Image features are necessary for object recognition. The fundamental morphological operations include Erosion and Dilation. Opening and Closing are also morphological operators. These operators are considering as basic operations in image processing algorithms. DILATION Dilation operator can be applied to binary and grey scale images. The objective of this operator is to enlarge the foreground and shrinks background. It gradually increases the boundaries of the region, while the small holes present in the image become smaller. It increases the brightness