Otsu's method of image segmentation is one of the best methods for threshold selection. With Otsu's method an optimum threshold is found by maximizing the between-class variance; Otsu algorithm is based on the gray-level histogram which is estimated by a sum of Gaussian distributions. In some type of images, image data does not best fit in a Gaussian distribution model. The objective of this study is to develop and compare two modified versions of Otsu method, one is based on Lognormal distribution (Otsu-Lognormal), while the other is based on Gamma distribution (Otsu-Gamma); the maximum between-cluster variance is modified based on each model. The two proposed methods were applied on several images and promising experimental results were obtained. Evaluation of the resulting segmented images shows that both Otsu-Gamma method and Otsu-Lognormal yield better estimation of the optimal threshold than does the original Otsu method with Gaussian distribution (Otsu).

Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

Accurate thresholding of skin cancer images is avery essential issue in medical diagnostic applications andbiometric authentication and verification systems [1], anotherimportant issue in such applications relates to itscomputational complexity. The objective of this study is todevelop a more accurate and faster solution for estimating theoptimal thresholding value of skin images. This work proposesa new algorithm for an Iterative Minimum Cross EntropyThresholding method based on Log-normal distribution(MCET-Lognormal), under the assumption that the data ofskin images is best modelled as a mixture of Log-normaldistributions. The proposed method was applied on bi-modalskin cancer images and promising experimental results wereobtained. Evaluation of the resulting segmented skin imagesshows that the proposed method yields better estimation of theoptimal threshold than does the same MCET method withGaussian distribution (MCET-Gaussian). It also reducescomputational time compared to sequential search techniques.

Minimum Cross Entropy Thresholding Using Entropy-Li Based on Log-normal Distribution for Skin Cancer Images

Thresholding is an an essential step in processing digital images for many applications. Accurate thresholding of digital images is crucial and the objective of this study is to develop a more accurate solution for estimating the optimal threshold value of digital images. This paper proposes a 3-dimensional entropic thresholding method based on a 3-dimensional histogram (GLLALE). The proposed method was applied on several test images and promising results were obtained. Evaluation of the resulting segmented images shows that the proposed method yields better estimation of the optimal threshold than does the 1D entropic thresholding of Kapur, and the 2D entropic thresholding methods of Abutaleb and Xiao.

A new image segmentation method based On 3-dimensional entropic thresholding using a 3-dimensional (GLLALE) histogram

Skin cancer is the most common cancer diagnosis and it is the most preventable cancer. Diagnosis of skin cancer would be improved if an accurate skin image segmentation is available. The process of image segmentation is a fundamental step in many applications of image processing, yet current methods and techniques for image segmentation necessitate particular domain knowledge to define well the region of the cancer. To estimate an optimal threshold for skin cancer images, thresholding is used as the principal approach of segmentation. We propose a new method for skin cancer segmentation using a Minimum Cross Entropy Thresholding (MCET) method. We applied this method on bimodal skin cancer images and obtained promising experimental results. The resulting segmented skin cancer images yielded better estimation of the optimal threshold than does the same MCET method with Poisson distribution.

Skin Cancer Segmentation with Entropy PAL MCET using Gaussian Distribution

Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions.

Novel fingerprint segmentation with Entropy-Li MCET using Log-normal distribution

There are some general procedures for image segmentation. One can apply different algorithms to create different clusterings of the data. Some clustering algorithms like k-means require initialization of parameters. Different initialization can lead to different data clusterings. In this paper, we explore the idea of evidence accumulation for combining the result of multiple clustering. Initially, data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of K-means. Taking the co-occurrences of pairs of patterns in the same cluster as means for their association, we deal with the image with the means. We compare our new method with the k-means. The experiment shows that our approach can achieve higher or comparable performance than the old method.

A New Method for Image Segmentation Based on Integration Technique

Image inpainting technique has been widely used for reconstructing damaged old photographs and removing unwanted objects from images. In this paper, we introduce an energy function based on object removal inpainting technology. In our system, we use the energy function to measure the quality of the reconstructed region, so a better image completion result is obtained. Several examples and comparisons are given to demonstrate the performing of gradient-based image completion approach.

An Improved Object Removal Inpainting Method

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A New Method for Image Segmentation Based on Integration Technique

An Improved Object Removal Inpainting Method