TWISTER SEGMENT MORPHOLOGICAL FILTERING. A NEW METHOD FOR LIVE ZEBRAFISH EMBRYOS CONFOCAL IMAGES PROCESSING. M.A.Luengo-Oroz § , E.Faure ‡ , B.Lombardot ‡ , R.Sance § , P.Bourgine ‡ , N.Peyri´ eras ♮ and A. Santos § § Biomedical Image Technologies lab., ETSI Telecomunicaci´ on, Universidad Polit´ ecnica de Madrid, Spain ‡ Centre de Recherche en Epist´ emologie Appliqu´ ee, CNRS - ´ Ecole Polytechnique, Paris, France ♮ DEPSN, CNRS - Institut de Neurobiologie Alfred Fessard, Gif-sur-Yvette, France ABSTRACT We propose an extension of the classical morphological filter- ing based on openings by line segment structuring elements. It consists in filtering a 3D+time image with the opening by all the possible rotations of a segment in the 4D space, giv- en an initial segment and a rotation angle. The method has been applied to remove noise from confocal laser scanning microscopy images of live zebrafish embryos engineered to fluorescently label all their cell membranes. Index Terms— Biomedical image processing, image restoration, morphological operations, microscopy. 1. INTRODUCTION Reconstructing the morphodynamics of cell division and pattern formation throughout embryonic development in live animals is of great interest for bio-medical research[1][2]. Among other applications, it would be the basis for a new gen- eration of pre-clinical drug testing with automated systems for investigating drug effects at the cellular level in vivo. The re- construction of cell morphodynamics greatly challenges in vi- vo imaging techniques as well as computer vision algorithms. The Embryomics project aims at fully reconstructing in space and time the cell lineage of the zebrafish (Danio rerio) em- bryo from the one cell stage throughout early steps of embryo- genesis. This is achieved through time-lapse laser scanning microscopy that produces z stacks of xy images at every time step t from live embryos engineered to highlight their cell structures. To segment cell structures and track cells requires very powerful methods of image treatment starting with im- age filtering. A large number of filtering methods have been described literature, nevertheless their suitability can be ques- tioned in terms of computational cost, parametric optimiza- tion and use of contextual information. We show here that an alternative filtering method based on membranes geometry This work is supported by the spanish grant FPI-CAM(0362/2005) and the european projects Embryomics (NEST 012916) and BioEmergences (NEST 028892). We thank all the members of these projects for our fruit- ful multidisciplinary interaction and the anonymous reviewer suggestions. which is an extension of a classical morphological filter, re- moves noise from live zebrafish embryo 4D images in a very efficient way, with a predictable computational cost. 2. TWISTER SEGMENT FILTERING 2.1. Preliminaries A grey-tone image can be represented by a function f : D f → T , where D f is a subset of Z 2 and T = {t min , ..., t max } is an ordered set of grey-levels. Let B be a subset of Z 2 and s ∈ N a scaling factor. sB is called structuring element B of size s. The basic morphological operators are dilation [δ B (f (x)) = sup y∈B {f (x − y)}] and erosion [ε B (f (x)) = ´ ınf −y∈B {f (x − y)}]. These two elementary operations can be composed together in openings [γ B (f )= δ B [ε B (f )]] and closings [ϕ B (f )= ε B [δ B (f )]]. The morphological opening (closing) filter out light (dark) structures from the images ac- cording to the predefined size and shape criterion of the struc- turing element. An algebraic opening γ is increasing, idempo- tent and anti-extensive. One important property of openings is that any supremum of openings is still an opening. This prop- erty provides the possibility of extracting specific geometrical structures of different classes in the same filtering process- ing (i.e. openings and closings with line segments have been widely used in image processing to extract lines in an image [3][4]). In this paper we use the same notation that has been used in [5], so an opening by a structuring element B is de- noted γ B . It is denoted also by l 1 n ,l 2 n ,l 3 n ,l 4 n the line segments of length n and respective orientation 0 0 , 45 0 , 90 0 , 135 0 . The operation Γ l n,d = ∨ i∈[1,d] γ l i n is an opening by line segments (see Fig.1). The two main drawbacks of this filtering is the computational cost (several independent openings and supre- mum calculation) and the selection of the size value n (a priori knowledge of the analyzed image may provide useful infor- mation for the n choice). The classical morphological alterna- tives to this approach are openings by reconstruction and area openings. Nevertheless both filters have their own drawbacks and the filtering by line segments is more easily parallelizable. V - 253 1-4244-1437-7/07/$20.00 ©2007 IEEE ICIP 2007