Retina vessels are important landmarks in fundus images, an accurate segmentation of the vessels may be useful for automated screening for several eye diseases or systematic diseases, such as diebetes. A new method is presented for automated segmentation of blood vessels in two-dimensional color fundus images. First, a coherence filter and a followed mean filter are applied to the green channel of the image. The green channel is selected because the vessels have the maximal contrast at the green channel. The coherence filter is to enhance the line strength of the original image and the mean filter is to discard the intensity variance among different regions. Since the vessels are darker than the around tissues depicted on the image, the pixels with small intensity are then retained as points of interest (POI). A new line fitting algorithm is proposed to identify line-like structures in each local circle of the POI. The proposed line fitting method is less sensitive to noise compared to the least squared fitting. The fitted lines with
higher scores are regarded as vessels. To evaluate the performance of the proposed method, a public available database DRIVE with 20 test images is selected for experiments. The mean accuracy on these images is 95.7% which is comparable to the state-of-art.
As important anatomical landmarks of the human lung, accurate lobe segmentation may be useful for characterizing
specific lung diseases (e.g., inflammatory, granulomatous, and neoplastic diseases). A number of investigations showed
that pulmonary fissures were often incomplete in image depiction, thereby leading to the computerized identification of
individual lobes a challenging task. Our purpose is to develop a fully automated algorithm for accurate identification of
individual lobes regardless of the integrity of pulmonary fissures. The underlying idea of the developed lobe
segmentation scheme is to use piecewise planes to approximate the detected fissures. After a rotation and a global
smoothing, a number of small planes were fitted using local fissures points. The local surfaces are finally combined for
lobe segmentation using a quadratic B-spline weighting strategy to assure that the segmentation is smooth. The
performance of the developed scheme was assessed by comparing with a manually created reference standard on a
dataset of 30 lung CT examinations. These examinations covered a number of lung diseases and were selected from a
large chronic obstructive pulmonary disease (COPD) dataset. The results indicate that our scheme of lobe segmentation
is efficient and accurate against incomplete fissures.