Mass Detection in Lung CT Images using Region Growing Segmentation and Decision Making based on Fuzzy Systems

Abstract

-Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. In order to help specialists in the search and recognition of the lung nodules in tomography images, a good number of research centers have been developed in computer-aided detection (CAD) systems for automating the procedures. This work aims at detecting lung nodules automatically through computerized tomography images. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images and, then, cancer recognition by FIS (Fuzzy Inference System).The proposed method consists of three steps. The first step was pre-processing for enhancing contrast, removing noise, and pictures less corrupted by LinearFiltering. In second step, the region growing segmentation method was used to segment the CT images. In third step, we have developed an expert system for decision making which differentiates between normal, benign, malignant or advanced abnormality findings. The FIS can be of great help in diagnosing any abnormality in the medical images. This step was done by extracting the features such as area and color (gray values) and given to the FIS as input. This system utilizes fuzzy membership functions which can be stated in the form of if-then rules for finding the type of the abnormality. Finally, the analysis step will be discussed and the accuracy of the method will be determined. Our experiments show that the average sensitivity of the proposed method is more than 95%.

A Review on Lung Nodule Detection in CT image using Image Processing Techniques

“…Some of the techniques are Gaussian filtering, median filtering, gabor filtering, linear filtering and histogram equalization. Gaussian filter is used for smoothing and hence to reduce the noise [1].Median filter [2,3] is preferred because the blurring of the edges in the image is less as compared to other filter [5,6]. Bilateral filter is used to enhance plate like structures whereas CED (Coherence enhancing diffusion) can preserve small spherical structures and enhance tubular structures [1].…”

“…Wavelet transform can describe an image in a various scales and noise can be reduced with the help of low pass filter. Thersholding [1,3,6] is used to segment the image in two regions based upon the pixel value difference between the object of interest in the image and the background and it will generate a binary image. Region growing method [4,7] causes back group elimination and other addition part such as bones to extract lung tissue and region of interest (ROI) in the lung image.…”

“…Area, perimeter [2]. Area, energy, contrast, entropy, correlation [3]. Normally the statistical texture analysis the texture features are computed from the statistical distribution of observed combinations of intensity points in each combination, statistics are classified as first order, second order etc.…”

Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Contextual Clustering Based Region Growing

“…Fuzzy self -organizing layer is used for grouping and clustering of nodule pixels in regions based on their membership. This layer would help in differentiating in nodule and non nodule clusters [6,8]. By performing different experiments, it is found that the proposed fuzzy neural network is more tolerant to the noisy data related to nodules.…”

“…Moreover, many researchers have used the existing thresholding techniques to segment various regions of interest. The most frequently used techniques used for segmentation are statistical, geometrical, structural, model-based, signal processing, spatial domain filters, Fourier domain filtering, Gabor and wavelet models [8]. A method in which it combines CC and region growing is used to segment the CT lung images effectively The main objective of our work is to increase the efficiency of detection with less computational cost than the previous techniques.…”