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Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme
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Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme

Category : Image Processing


Sub Category : BIOMEDICAL


Project Code : IMP07


Project Abstract

Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          The main objective is to develop a technique so that lung nodules can be detected using X-ray imaging at an early stage.

          The purpose is to develop the CADe scheme with improved sensitivity and specificity by use of Virtual Dual Energy (VDE) chest radiographs. Ribs and clavicles in the chest radiographs (X-ray images) are suppressed with MTANN.

PROPOSED CONCEPT:

          The main objective is to develop a technique so that lung nodules can be detected using computed tomography (CT) imaging method.

          The purpose is to develop the CADe scheme with improved sensitivity and specificity by use of Vector quantization (VQ).

EXISTING  TECHNIQUE :

          MULTIRESOLUTION MASSIVE TRAINING ARTIFICIAL NEURAL NETWORK (MTANN)

PROPOSED ALGORITHM:

          VECTOR QUANTIZATION (VQ)

TECHNIQUE DEFINITION:

         MTANN is an image processing technique used for suppressing the contrast parameter of ribs and clavicles.

         An MTANN is a nonlinear filter which is trained by input chest radiograph images and the corresponding training images. Using Dual-Energy subtraction, bones like images are obtained.

ALGORITHM DEFINITION:

           Hierarchical VQ scheme is doing automatic detection and segmentation of INCs.

          The general VQ framework evolves two processes: 1) the training process which determines the set of codebook vector according to the probability of the input data; and 2) the encoding process which assigns input vectors to the codebook vectors.

DRAWBACKS:

          The existing methods are not faster and adaptive.

          The ribs may cause unwanted error in the detection of pulmonary nodules.

          The processing time of x-ray image is more and so it delay’s the result of identification of pulmonary nodule.

·         Accuracy as well as efficiency is low.

ADVANTAGES:

          This project demonstrates fast and adaptive detection of pulmonary nodules in chest CT scans.

          Compared with existing CADe systems evaluated on the same lung image LIDC database, our approach showed a comparable detection capability but a lower computational cost.

·         The proposed hierarchical INCs detection approach is fast, adaptive, and fully automatic.


 
 
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