Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: This paper introduces an image denoising algorithm based on generalized Srivastava-Owa fractional differential operator for removing Gaussian noise in digital images. The structures of nxn fractional masks are constructed by this algorithm. Experiments show that, the capability of the denoising algorithm by fractional differential-based approach appears efficient to smooth the Gaussian noisy images for different noisy levels. The denoising performance is measured by using peak signal to noise ratio (PSNR) for the denoising images. The results showed an improved performance (higher PSNR values) when compared with standard Gaussian smoothing filter.
Abstract: Due to availability of powerful image processing software
and improvement of human computer knowledge, it becomes
easy to tamper images. Manipulation of digital images in different
fields like court of law and medical imaging create a serious problem
nowadays. Copy-move forgery is one of the most common types
of forgery which copies some part of the image and pastes it to
another part of the same image to cover an important scene. In
this paper, a copy-move forgery detection method proposed based
on Fourier transform to detect forgeries. Firstly, image is divided to
same size blocks and Fourier transform is performed on each block.
Similarity in the Fourier transform between different blocks provides
an indication of the copy-move operation. The experimental results
prove that the proposed method works on reasonable time and works
well for gray scale and colour images. Computational complexity
reduced by using Fourier transform in this method.