Automated detection of blob structures by Hessian analysis and object scale

Automated detection of blob-like structures is desirable in many biomedical applications such as nodule detection in radiographs and CT images, lymph nodes detection in CT images, and cell counting or tracking in biological images. Multiscale analysis of Hessian matrix is widely used for enhancement or detection of blob-like structures in two-dimensional (2D) and three-dimensional (3D) images. We proposed a new blob detector and a new detection response measure, blobness, based on eigenvalues of the Hessian matrix and local object scale. Pixels with higher blobness are clustered as detected blobs. We evaluated our method by comparison with two existing methods on both simulated and real images. Our results indicated that our automated blob detector had better performance on those images especially when the blobs were close to each other. Our method can be easily extended to 3D for computer-aided detection of blob-like structures in medical images.