Space-invariant signature algorithm processing of ultrasound images for the detection and localization of early abnormalities in animal tissues

Abstract

In this paper we present an innovative space-variance approach named as “Space-Invariant Signature Algorithm (SISA)” for processing images from active systems, such as cancer cells, tumor growth, and dead cells, for the detection and localization of abnormalities at an early stage. In this paper, a SISA processing algorithm is developed, and this algorithm is tested on animal tissues such as pigs and chicken tissues. The abnormality in an active system can be defined as the obstacle or a failure which impedes the activities in tissues such as smooth flow of blood or electrical signals etc. Due to this impeding nature of the abnormality, some parameter perturbations are induced. In this paper using the SISA approach, these perturbations were detected in a preliminary experiment on animal tissues. The degree and position of the space-variance helps us in the detection and localization of abnormality even at an early (incipient) stage. The space-variance signature pattern is named as a ‘SISA signature pattern’. In the absence of any abnormality, the signature pattern is space invariant, whereas, in the presences of any abnormality, the SISA signature pattern varies in the space (space-variant). The basic experimental studies on animal tissues using ultra sound imaging strongly suggest a possible use of the SISA approach as a non-invasive method for the detection and localization of abnormalities in biological tissues such as cancer cells non-invasively.