Vessel for Their Seed

Because seeds are singular - from one seed grows an entire plant laden with more seed - there was unlikely to be any speculation about two seeds and fusion. Men helped, but mother was the source of life. This figurine was found in a grain bin or, as I would call it, a seed store. It comes from a very important archaeological site in Turkey. This theme, of powerful women with two felines can be seen throughout the early Neolithic from India to Palestine, and beyond.

Also found here was a female figurine with a hole in her back through which you can still see a seed. It was placed low - about the level of the womb - and it raises the question of how many other female figurines were made with seeds placed inside them but, because they're not damaged in the same way, we don't know the seed is there. Catal Hoyuk seems to have been an egalitarian society, both in terms of men and women, and in terms of there not being an upper class with palaces. This stone carving is the first known that might illustrate where babies come from: This picture shows a detail from a group of 7, year old figurines: They were all found together in a large vessel, stored there for safety.

They have been called 'an assembly' or 'a council', but of course we have no idea what this scene was meant to represent. It does though seem they are engaged in a communal activity, presumably for the common good. Impressive female figurines have been found all over the world. Archaeologists love to use the word "fertility" to descibe females - as if that word had any meaning. But as archaeologists don't discuss reproduction theory, it's hard to work out what they do mean. If they even know. If I sound annoyed that's because I have asked many archaeologists about their specialist areas: And the disappointing thing is they have no idea what I am even asking.

The only reference archaeologists seem to make to reproduction theory is to say "men discoverd their role in reproduction around 10, BC. Before then everyone was ignorant. So what did they discover?

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That is what archaeologists need to ask themselves. Can we have some clarity please? During the Neolithic people settled down and developed their local, individual, artistic and symbolic styles. This figurine from Cyprus is 16 inches high and, as you can see, has a very well defined vulva at the breast level.

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seed vessel - Dictionary Definition : www.newyorkethnicfood.com

An Overview of Biodiversity. Have vascular tissue, true roots, wood in many Have pollen in flowers that attract pollinators Pollen and seeds have allowed them to be less dependent on water for reproduction than any other group of plants. Flowers are very diverse! The proposed RG technique was tested on Digital Retinal Images for Vessel Extraction database for three different initial seed sets and evaluated against the manual segmentation of retinal images available at this database. Three quantitative criteria including accuracy, true positive rate and false positive rate, were considered to evaluate this method.

The visual scrutiny of the segmentation results and the quantitative criteria show that, using cellular automata for extracting the blood vessels is promising. However, the important point at here is that the correct initial seeds have an effective role on the final results of segmentation. Segmentation; separation of structures of interest from the background and each other, is an essential analysis function for which numerous algorithms have been developed in the field of image processing.

In medical imaging, automated delineation of different image components is used for analyzing anatomical structure and tissue types, spatial distribution of function and activity, and pathological regions. Since segmentation requires classification of pixels, it is often treated as a pattern recognition PR problem and addressed with related techniques.

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Especially in medical imaging, where variability in the data may be high, PR techniques that provide flexibility and convenient automation are of special interest. Pattern recognition techniques deal with the automatic detection or classification of objects or features. Region growing RG or grow-cut is one of the PR techniques. This approach is a frequently used segmentation method. However, it has a main disadvantage. It often requires user-supplied seed points. If a seed point is selected outside the region of interests ROIs , the final segmentation result would be definitely incorrect.

Several papers proposed RG techniques for image segmentation. Some of them proposed an automatic algorithm for seed selection, some papers considered semi-automatic approaches, and another one selected the seeds in a manual manner. There are several researches that applied automatic approaches for seed selection. For example, Shan et al. This method is composed of five steps: For selecting automatic seed, the histogram of each band is analyzed to obtain a set of representative pixel values, and the seeds are generated with all the image pixels with representative gray values.

Three methods have been proposed to generate seeds automatically. The second one finds the edges of the image and obtains the initial seeds from the centroid of the color edges. Moreover, the third method extends the second method to deal with noise by applying an image smoothing filter. To select the seeds automatically, for RG, they used a competitive learning neural network to do the initial segmentation.

In fact, they set the skeletons of foreground and background as the seeded regions for object and background, respectively. Shih and Cheng[ 8 ] proposed an automatic seeded RG algorithm for color image segmentation.

Dalmau and Alarcon,[ 9 ] proposed a segmentation strategy using cellular automata CA with an automatic thresholding scheme for seed selection to extract of the retinal blood vessels RBVs ; called matched filter with cellular automata MFCA. In three-dimensional view, user only needs to specify certain two-dimensional image pixels as seeds in the multi-planar reformation.

There are several examples of CAS techniques[ 14 , 15 , 16 , 17 , 18 ] too, that started the segmentation process using manually seed selection. This paper is organized as follows. At first, the proposed method for RBVs extraction including automatic seeding, and CAS technique for final extraction is described. Then, experimental results are presented and compared with manual extracted RBVs. Finally, the discussion and conclusion will be provided. Figure 1 shows the block diagram of proposed RBVs extraction method.

According to this figure, at first we consider an ASPS algorithm to determine some starting points for segmentation step. Next, the CAS technique is applied, and pixels are classified into vessel or nonvessel; background pixels. For selecting seed points automatically, two main criteria were considered. A seed pixel candidate must have the similarity higher than a threshold value. A seed pixel candidate must have the maximum spatial proximity to its neighbors less than a threshold value. Each pixel that simultaneously satisfies both conditions 1 and 2, is selected as a seed point. Connected components of seed pixels are taken as one seed.

Therefore, the selected seeds can be one pixel or one region with several pixels. For example, a specific measure of value similarity condition VSC between two pixels is the difference among the gray values, and a specific measure of spatial proximity condition SPC is Euclidean distance.

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The variance of the gray values in a region and the compactness of the region can also be used as measures of VSC and SPC of pixels within a region, respectively. In fact, a retinal image is specified in RGB color space. This color space is suitable for color display but, because of its high correlation among R, G, and B components, it is not good for color analysis.

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Furthermore, the distance in RGB color space does not represent the perceptual difference in a uniform scale. C b is the difference between the blue component and a reference value, and C r is the difference between the red component and a reference value. For VSC, considering eight neighbors surrounding each pixel, the standard deviations of Y , C b , and C r components are calculated by: The total standard deviation is:. The similarity of a pixel to its neighbors is defined as:. This condition checks whether the seed pixel has high similarity to its neighbors or not.

For a seed point, S factor is higher than a threshold value. In order to select the threshold value automatically for this condition, we use Otsu's method: Condition 2 makes sure that the seed pixel is not on the boundary of two regions. For this condition, we select an experimental value, T Ex , as the threshold:.

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Note that by choosing a low value for this threshold, a smaller number of pixels will be classified as seeds and some parts of vessels may be missed; oppositely, a higher number of pixels will be classified as seeds, and different regions may be connected. About the retinal images, we must know that not all of the pixels in these images should be considered in the seed selection process. These pixels are those belong to the dark surrounding region in the retinal image. Therefore, we need to generate a mask that determines the main pixels or the same ROI! Ulam and von Neumann originally conceived CA in the s to provide a formal framework for investigating the behavior of complex, extended systems.

seed vessel

CAs are dynamical systems in which space and time are discrete. In general, we have: The state of a cell is determined by the previous states of a surrounding neighborhood of cells. Commonly used neighborhood systems N are Von Neumann neighborhood or Moore neighborhood that are described in Eqs. The state of a cell is updated synchronously in discrete time steps according to a local, identical interaction rule.