Quoit filter-a new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray CT

Abstract

We propose a new algorithm named "Qiroit,filter (QPlter~ " to e.vtruct the isolated but low amplitude shudow located in the background which h a y extremely high amplitdeJuctuution. @filter is a kind of mathematical morphology and its formulation is quite simple. This simplicity brings about a unique merit that output from this filter is analytically eqmssivefior the case of analyical input shupes like ball, cone, or rotation of cosine function, which have churucteristics of rotation qwimetry a d monotonic decreasing from the origin.This Q-filter is composed of two sequential operation named Q Trans. and Q Inv. Trans. , each operation having an interesting characteristics as follows : Q Trans. corresponds to e-rtract feature Imrameter like a matched filter from input image having non ideal isolated shudow, and Q Inv. Trans. correspond.Y to restore isolated image using extracted feature paranieter. This filter is applied to detect the cancer candidate shadow automatically in the CT cross sections of lung areas. aiming to reduce drastically the number of cross sections to be diagnosed by the doctor.

Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes

“…From the CT scans, lung regions are extracted by a threshold-based technique [4], and then initial nodule candidates are detected from the lung regions by our methods [8,27,28]. The number of nodule candidates per scan is 93.8.…”

“…Figure 1 illustrates the overview. First, initial nodule candidates are detected from individual slice sections of a thoracic CT scan by our previous detection methods [8,27,28], and then square areas of a certain size are settled on the slice sections so as to comprise the nodule candidates. White squares on a slice section in the leftmost box in the figure show examples of such square areas.…”

“…Morphological [7] image filters [8][9][10] are conventional approaches. In the work [11], a multiple-thresholding technique was used to detect nodules that had peaked intensity distribution.…”

A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

“…This lead to the requirement of lung nodule detection in chest Computer Tomography (CT) images (Armato et al, 2001) in advance. Thus the Computer Aided Diagnosis (CAD) (Yamamoto et al, 1996;Wiemker et al, 2002) system is very essential for early detection of lung cancer. Early finding of the disease is critical but the truth remains that only 20% of cases are detected in the first phase.…”

“…The experimental results indicate that the proposed algorithm detected lung cancer candidates successfully. Yamamoto et al (2000) explained Computer aided diagnosis system with functions to assist comparative reading for lung cancer based on helical CT image. The author have reported that a prototype Computer-Aided Diagnosis (CAD) system (Kanazawa et al, 1998) to automatically detect suspicious regions from chest CT images had been presented and the CT screening system used was a TCT-900 super helix of the Toshiba Corporation.…”