JPH0649195B2 - Microorganism detection device - Google Patents
Microorganism detection deviceInfo
- Publication number
- JPH0649195B2 JPH0649195B2 JP18960385A JP18960385A JPH0649195B2 JP H0649195 B2 JPH0649195 B2 JP H0649195B2 JP 18960385 A JP18960385 A JP 18960385A JP 18960385 A JP18960385 A JP 18960385A JP H0649195 B2 JPH0649195 B2 JP H0649195B2
- Authority
- JP
- Japan
- Prior art keywords
- microorganism
- image
- microorganisms
- luminance information
- filamentous
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime
Links
- 244000005700 microbiome Species 0.000 title claims description 53
- 238000001514 detection method Methods 0.000 title description 3
- 230000000813 microbial effect Effects 0.000 claims description 9
- 241000233866 Fungi Species 0.000 description 23
- 238000000034 method Methods 0.000 description 18
- 239000010802 sludge Substances 0.000 description 13
- 230000003287 optical effect Effects 0.000 description 12
- 238000004062 sedimentation Methods 0.000 description 8
- 239000011521 glass Substances 0.000 description 7
- 239000007788 liquid Substances 0.000 description 6
- 239000010865 sewage Substances 0.000 description 6
- 239000007791 liquid phase Substances 0.000 description 5
- 244000005706 microflora Species 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000003384 imaging method Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000005273 aeration Methods 0.000 description 3
- 239000003242 anti bacterial agent Substances 0.000 description 3
- 229940088710 antibiotic agent Drugs 0.000 description 3
- 230000002538 fungal effect Effects 0.000 description 3
- 230000010365 information processing Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 238000012258 culturing Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000005416 organic matter Substances 0.000 description 2
- 241000736262 Microbiota Species 0.000 description 1
- 229930182555 Penicillin Natural products 0.000 description 1
- JGSARLDLIJGVTE-MBNYWOFBSA-N Penicillin G Chemical compound N([C@H]1[C@H]2SC([C@@H](N2C1=O)C(O)=O)(C)C)C(=O)CC1=CC=CC=C1 JGSARLDLIJGVTE-MBNYWOFBSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000005056 compaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005189 flocculation Methods 0.000 description 1
- 230000016615 flocculation Effects 0.000 description 1
- 244000144992 flock Species 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000004313 glare Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000001000 micrograph Methods 0.000 description 1
- 238000006864 oxidative decomposition reaction Methods 0.000 description 1
- 229940049954 penicillin Drugs 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000012085 test solution Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F3/00—Biological treatment of water, waste water, or sewage
- C02F3/34—Biological treatment of water, waste water, or sewage characterised by the microorganisms used
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means
- G01N15/0227—Investigating particle size or size distribution by optical means using imaging; using holography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N2015/0042—Investigating dispersion of solids
- G01N2015/0053—Investigating dispersion of solids in liquids, e.g. trouble
- G01N2015/0057—Investigating dispersion of solids in liquids, e.g. trouble of filaments in liquids
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Microbiology (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Environmental & Geological Engineering (AREA)
- Water Supply & Treatment (AREA)
- Hydrology & Water Resources (AREA)
- Organic Chemistry (AREA)
- Dispersion Chemistry (AREA)
- Biodiversity & Conservation Biology (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
- Activated Sludge Processes (AREA)
Description
【発明の詳細な説明】 〔発明の利用分野〕 本発明は、微生物相の出現種類及び出現量を画像処理技
術を用いて検出する微生物検出装置に関する。Description: FIELD OF THE INVENTION The present invention relates to a microorganism detecting device for detecting the type and amount of appearance of microflora using image processing technology.
大量の微生物を利用する代表的な例として、生物学的下
水処理法が知られている。活性汚泥法は生物学的下水処
理法の1つで、好気性微生物の酸化分解作用を利用して
下水中の有機物を処理する方法である。プロセスは好気
性状態に維持される曝気槽と、微生物を重力沈降させる
沈降池とから構成される。このプロセスの運転管理上、
特に重要なことは沈降性の良好な微生物を育成すること
である。これは、沈降性の悪い微生物が出現すると、沈
降池から流出して処理水質を悪化させるだけでなく、プ
ロセス系内の総微生物量の低下を招いて処理不能に陥ら
せるためである。As a typical example of utilizing a large amount of microorganisms, a biological sewage treatment method is known. The activated sludge method is one of biological sewage treatment methods, and is a method of treating organic substances in sewage by utilizing the oxidative decomposition action of aerobic microorganisms. The process consists of an aeration tank maintained in an aerobic condition and a settling basin for gravity settling of microorganisms. Due to the operational management of this process,
What is particularly important is to grow microorganisms having a good sedimentation property. This is because when microorganisms having poor sedimentation properties appear, not only do they flow out of the sedimentation pond and the quality of the treated water deteriorates, but also the total amount of microorganisms in the process system decreases and the treatment becomes impossible.
ところで、一般に活性汚泥法がよく出現する微生物の種
類は50種程度と云われている。これらの微生物群の総
称である活性汚泥を大別すると、2つのタイプの微生物
に分類できる。1つは凝集性の有るズーグレア(Zoogloe
a)タイプの微生物で、良好なフロツクを形成するための
沈降性に優れている。他方は糸状性の微生物で、フロツ
ク相互の接近を妨害し、沈降性及び圧密性に劣つてい
る。この糸状性微生物が異常に増殖すると、バルキング
状態(汚泥膨化)を引き起こし、沈殿池で固液分離がで
きず、活性汚泥が流出する。By the way, generally, it is said that there are about 50 kinds of microorganisms in which the activated sludge method often appears. The activated sludge, which is a general term for these microorganism groups, can be roughly classified into two types. One is the cohesive zoo glare (Zoogloe
It is a) type microorganism and has excellent sedimentation property for forming good flocs. The other is a filamentous microorganism, which impedes the mutual access of flocs and is inferior in sedimentation and compaction. If this filamentous microorganism grows abnormally, it causes a bulking state (sludge expansion), solid-liquid separation is not possible in the sedimentation tank, and activated sludge flows out.
ズーグレア性微生物と糸状性微生物の増殖は曝気槽にお
ける有機物負荷(単位活性汚泥重量当りの有機物量)や
曝気条件等により異なる。したがつて、流入条件が一日
単位あるいは季節等で大きく変化する下水処理場では安
定したフロツクを形成する微生物相の維持管理が大切で
ある。そのためには、活性汚泥の状態を把握することが
必要となる。The growth of zooglare microorganisms and filamentous microorganisms differs depending on the organic matter load (the amount of organic matter per unit activated sludge weight) in the aeration tank and the aeration conditions. Therefore, it is important to maintain and manage the microbial flora that forms stable flocs in sewage treatment plants where the inflow conditions change drastically on a daily basis or in seasons. For that purpose, it is necessary to understand the state of activated sludge.
活性汚泥の状態を把握するには通常光学顕微鏡が用いら
れている。顕微鏡像は多くの情報を含んでおり、出現し
ている微生物の種類やその数を観察することにより活性
汚泥の状態を知ることができる。従来、微生物の種類や
数を測定するには顕微鏡像そのものを目視で観察する
か、あるいは写真撮影してその撮影結果を目視で観察す
る方法が採られている。しかし、この情報を読みとるに
は熟練を要し、また、微生物に詳しい熟練オペレータで
も長時間を費し頻度高い観察が困難である。特に、糸状
性微生物の出現数は撮像結果から個々の長さを手分析に
より割り出し、総長を求めるという苦労がある。したが
つて、顕微鏡像による微生物相の解析には専門知識を持
ち熟練オペレータでも長時間を要し、頻繁に観察するこ
とができず有効な情報が十分に生かされていないのが実
状である。An optical microscope is usually used to grasp the state of activated sludge. The microscopic image contains a lot of information, and the state of activated sludge can be known by observing the type and number of emerging microorganisms. Conventionally, in order to measure the type and number of microorganisms, a method of visually observing a microscopic image itself, or taking a photograph and visually observing the photographing result has been adopted. However, reading this information requires skill, and even a skilled operator who is familiar with microorganisms spends a lot of time and makes frequent observation difficult. In particular, the number of appearance of filamentous microorganisms is difficult to obtain by calculating the total length by manually analyzing the individual length from the imaging result. Therefore, it is the actual situation that the analysis of the microflora by microscopic images requires a long time even for an expert operator who has specialized knowledge and cannot be frequently observed, and effective information is not sufficiently utilized.
一方、適用対象が他の微生物培養プロセスではペニシリ
ンなどの抗生物質を生産する糸状性微生物を培養した
り、また糸状性微生物を利用して発酵させるようにして
いる。これらのプロセスでは、糸状性微生物を増殖させ
て、糸状性微生物から代謝される抗生物質を得るのが目
的であるが、抗生物質量を短時間で測定できないので、
糸状性微生物が増殖しているかを監視する必要がある。On the other hand, in other microbial culturing processes to which the invention is applied, filamentous microorganisms that produce antibiotics such as penicillin are cultivated or fermented using filamentous microorganisms. In these processes, the purpose is to grow filamentous microorganisms and obtain antibiotics that are metabolized from filamentous microorganisms, but since the amount of antibiotics cannot be measured in a short time,
It is necessary to monitor the growth of filamentous microorganisms.
従来は糸状性微生物をサンプリングしてこれを乾燥し、
この重量を計るようにしている。しかしながらこの測定
方法は下水処理プロセスと同様に監視員によるマニアル
操作のため、熟練した監視員が測定しても1回の測定に
数時間を要する。Conventionally, filamentous microorganisms are sampled and dried,
I try to weigh this. However, since this measuring method is a manual operation by an observer as in the case of the sewage treatment process, even if a skilled observer makes a measurement, it takes several hours for one measurement.
一方最近は、特開昭60-31889号公報に記載されているよ
うに、撮像装置として工業用テレビカメラ(ITV)で
微生物を画像情報として捕え、画像処理技術により微生
物を流量的に検出する方法も考察されている。しかし、
微生物の輝度と背景の輝度の差が小さい場合、光学系に
よる背景輝度は完全に均一にはできず光ムラが生じ、微
生物のみを検出するのは困難である。On the other hand, recently, as described in Japanese Patent Laid-Open No. 60-31889, a method of capturing microbes as image information with an industrial television camera (ITV) as an imaging device and detecting the microbes in a flow rate by image processing technology. Are also considered. But,
When the difference between the brightness of the microorganisms and the brightness of the background is small, the background brightness of the optical system cannot be perfectly uniform, and light unevenness occurs, and it is difficult to detect only the microorganisms.
本発明の目的は、微生物の顕微鏡像を画像処理して、微
生物量を短時間に解析し、自動的に検出できる微生物検
出装置を提供することにある。It is an object of the present invention to provide a microorganism detecting apparatus capable of performing image processing on a microscopic image of a microorganism, analyzing the amount of the microorganism in a short time, and automatically detecting the amount.
本発明の特徴は、顕微鏡像を輝度情報に変換し微生物サ
ンプル前の背景輝度情報をサンプル後の輝度情報から差
引くと、微生物のみの輝度情報が得られる点に着目し、
その輝度情報に任意の輝度レベルを設定することで、目
的とする微生物に対応した画素数を抽出し、微生物相の
状態を自動的に検出できるようにしたことにある。The feature of the present invention is that when a microscope image is converted into luminance information and the background luminance information before the microbial sample is subtracted from the luminance information after the sample, the luminance information of only the microorganism is obtained,
By setting an arbitrary brightness level in the brightness information, the number of pixels corresponding to the target microorganism is extracted and the state of the microflora can be automatically detected.
第1図に本発明の一実施例を示す。 FIG. 1 shows an embodiment of the present invention.
活性汚泥などの検液をサンプリング管101を介して採
集して、ポンプ103によりスライドグラス1に導く。
検液は観察された後排液管102から排出する。スライ
ドグラス1の上には、わずかなすき間を有して、例えば
光学顕微鏡のような像拡大光学装置2が配置されてい
る。スライドグラス1は光学顕微鏡の焦点が合うような
厚さの薄いものが望ましい。なお、光学顕微鏡の定法に
従つて検液をプレパラートに定量採集し検鏡することも
できる。採集した検液を、光学顕微鏡2により微生物相
を拡大すると、コントラストのある原画像を作製でき
る。光学顕微鏡が明視野のときは検鏡対象は暗く、背景
は明るくうつる。A test liquid such as activated sludge is collected through a sampling tube 101 and is guided to a slide glass 1 by a pump 103.
After the test liquid is observed, it is discharged from the drain pipe 102. An image magnifying optical device 2 such as an optical microscope is arranged on the slide glass 1 with a slight gap. The slide glass 1 is preferably thin so that the optical microscope can be focused on it. It is also possible to quantitatively collect the test liquid in a preparation according to a standard method of an optical microscope and perform a microscopic examination. By enlarging the microbiota of the collected test liquid with the optical microscope 2, an original image with contrast can be produced. When the optical microscope is in the bright field, the object to be examined is dark and the background is bright.
原画像は、明るい背景である液相部の中に暗い微生物相
部が存在する。原画像は例えば工業用テレビカメラ(以
下ITVと称する)のような撮像装置3により輝度情報
に変換される。ITV3で得た輝度情報は、情報処理装
置4で処理されモニタ5に表示される。第3図にITV
3よりの輝度情報に変換された活性汚泥の画像例を示
す。Zは活性汚泥フロツクのうち凝集性及び沈降性に優
れた、例えばズーグレア性微生物塊で、Fは沈降性の悪
い糸状菌を示す。Bは背景の液相部である。なお、微生
物塊Zにおけるハツチングを施した部分は黒く見えるこ
とを意味している。ここでNは例えば光学系やスライド
グラスに付着した汚れまたは傷によるノイズである。The original image has a dark microbial fauna in the liquid fauna that is the light background. The original image is converted into luminance information by an imaging device 3 such as an industrial television camera (hereinafter referred to as ITV). The brightness information obtained by the ITV 3 is processed by the information processing device 4 and displayed on the monitor 5. ITV in Figure 3
The example of the image of the activated sludge converted into the luminance information from 3 is shown. Z represents, for example, a zooglare microbial mass having excellent flocculation and sedimentation properties among the activated sludge flocs, and F represents a filamentous fungus having poor sedimentation properties. B is the background liquid phase part. It should be noted that the hatched portion of the microbial mass Z looks black. Here, N is noise due to dirt or scratches attached to the optical system or the slide glass, for example.
情報処理装置4の詳細を第2図に示す。Details of the information processing device 4 are shown in FIG.
第2図において、ITV3からの輝度情報は、A/D変
換器40でデジタル信号に変換され画像メモリ41に記
憶される。微生物が存在しない真水を検液とした場合、
ITV3による撮像画像は第4図のように背景のみにな
る。背景は液相部B,ノイズNからなる。背景画像情報
はA/D変換され背景輝度情報として画像メモリ41を
介して、補助画像メモリ42に記憶される。補助メモリ
41は活性汚泥中の微生物検出処理の前処理として、ス
ライドグラス1,顕微鏡2の状態を記憶することにな
る。In FIG. 2, the brightness information from the ITV 3 is converted into a digital signal by the A / D converter 40 and stored in the image memory 41. When using fresh water with no microorganisms as the test solution,
The captured image by ITV3 is only the background as shown in FIG. The background consists of a liquid phase portion B and noise N. The background image information is A / D converted and stored as background luminance information in the auxiliary image memory 42 via the image memory 41. The auxiliary memory 41 stores the states of the slide glass 1 and the microscope 2 as a pretreatment of the microorganism detection treatment in the activated sludge.
第5図は、第3図のA−A′線上の輝度分布を示したも
のである。MAXは喜怒の最大値を表わす。液相部Bの
輝度は高く、フロツク部Zの輝度は低い値を示す。両者
の中間が系状菌Fの輝度である。FIG. 5 shows the luminance distribution on the line AA 'in FIG. MAX represents the maximum value of emotions. The liquid phase portion B has a high luminance, and the flock portion Z has a low luminance. The brightness of the filamentous fungus F is intermediate between the two.
糸状菌Fを輝度情報を用いて抽出するためにはあらかじ
め液相部B及びフロツク部Zの輝度レベルを判定する必
要がある。In order to extract the filamentous fungus F using the luminance information, it is necessary to determine the luminance levels of the liquid phase portion B and the flot portion Z in advance.
第6図に示すように、液相部Bの輝度は光ムラのため不
均一であり、第5図に示す糸状菌Fの輝度レベルも不均
一となる。又ノイズNの輝度SN は糸状菌と類似してい
るため、糸状菌として抽出される可能性が大きい。As shown in FIG. 6, the brightness of the liquid phase portion B is non-uniform due to light unevenness, and the brightness level of the filamentous fungus F shown in FIG. 5 is also non-uniform. Further, since the luminance S N of the noise N is similar to that of the filamentous fungus, there is a high possibility that it will be extracted as the filamentous fungus.
i行j列の画素の輝度信号を、第3図,第4図の各々に
ついてSij,Wijとする。第2図において、Sijは画素
メモリ41に対応し、Wijは補助画素メモリ42に対応
している。輝度補正回路43は、輝度レベルの最大値M
AXを用いて(1)式に示す背景輝度の影響を除去した
補正輝度情報Cijを計算する。The luminance signals of the pixels in the i-th row and the j-th column are S ij and W ij in each of FIGS. 3 and 4. In FIG. 2, S ij corresponds to the pixel memory 41, and W ij corresponds to the auxiliary pixel memory 42. The brightness correction circuit 43 has a maximum brightness value M.
Using AX, the corrected luminance information C ij from which the influence of the background luminance shown in the equation (1) is removed is calculated.
Cij=Sij+(MAX+Wij) …(1) 第7図に補正輝度情報Cijで表わされる画像を示し、第
8図には、第7図画像のA−A′線上の輝度分布を示
す。補正輝度分布では、背景輝度がMAXで均一とな
り、ノイズNも除去される。C ij = S ij + (MAX + W ij ) ... (1) FIG. 7 shows an image represented by the corrected luminance information C ij , and FIG. 8 shows the luminance distribution on the line AA ′ of the image of FIG. Show. In the corrected luminance distribution, the background luminance is MAX and uniform, and the noise N is also removed.
輝度レベル判定回路44は、第8図輝度分布より、輝度
レベルの最大値SB と最小値SZ を取り出す回路であ
る。最大値SB は背景輝度が対象となり(2)式 SB=MAX …(2) で表わされ、最小値Sx はフロツク部Zの平均値が対象
となる。The brightness level determination circuit 44 is a circuit for extracting the maximum value S B and the minimum value S Z of the brightness level from the brightness distribution of FIG. The maximum value S B is the background brightness and is expressed by the formula (2) S B = MAX (2), and the minimum value S x is the average value of the block portion Z.
閾値設定回路45はフロツク部Zよりも高い輝度でかつ
糸状菌Fよりも低い輝度レベルSl と糸状菌Fよりも高
い輝度で背景輝度SB よりも低い輝度レベルSh を設定
する。設定レベルSl ,Sh の範囲は(3)式で示され
る。Threshold value setting circuit 45 sets a brightness level lower S h than the background luminance S B at a higher luminance than the lower luminance level S l and fungal F than a high brightness and filamentous fungi F than Furotsuku portion Z. The range of the set levels S l and S h is shown by the equation (3).
SB>Sl>Sh>SX (3) 2値化回路46は、糸状菌Fを補正輝度情報より抽出す
る。すなわち、i行J列の画素の持つ輝度情報Cijを
(4),(5)式により2値化し糸状菌Fに対応する画
素のみを抽出する。S B > S l > S h > S X (3) The binarization circuit 46 extracts the filamentous fungus F from the corrected luminance information. That is, the luminance information C ij of the pixel in the i-th row and the J-th column is binarized by the expressions (4) and (5), and only the pixel corresponding to the filamentous fungus F is extracted.
Sl≦Cij≦Shのときfij=1 …(4) Cij<SlまたはCij>Sh のときfij=0 …(5) ここでfijは糸状菌に対応する画素を表わす。2値化回
路46により糸状菌Fに対応する画素のみ信号を“1”
レベルとする。第8図の糸状菌Fを2値化抽出処理例を
第9図に、第7図微生物相から糸状菌Fを抽出した例を
第10図にそれぞれ示す。第10図のように抽出された
糸状菌Fは、積算回路47で、細線化処理を施され、幅
が1画素の線に処理される。以上の処理を行つた後、画
素信号が“1”レベルのfijをi行,j列の各々におい
て繰返し加算し、一画面全体における糸状菌Fの長さf
v を求める。画素fijの加算は加算回路48において
(6)式に従い求める。When S l ≦ C ij ≦ S h , f ij = 1 (4) When C ij <S 1 or C ij > S h , f ij = 0 (5) where f ij is a pixel corresponding to a filamentous fungus Represents Only the pixel corresponding to the filamentous fungus F has a signal "1" by the binarization circuit 46.
Level. FIG. 9 shows an example of binarizing and extracting the filamentous fungus F of FIG. 8 and FIG. 10 shows an example of the filamentous fungus F extracted from the microflora of FIG. 7. The filamentous fungus F extracted as shown in FIG. 10 is subjected to a thinning process by the integrating circuit 47 to be processed into a line having a width of 1 pixel. After performing the above processing, f ij of which the pixel signal is “1” level is repeatedly added in each of the i-th row and the j-th column to obtain the length f of the filamentous fungus F in the entire screen.
ask for v The addition of the pixels f ij is obtained by the addition circuit 48 according to the equation (6).
一画面のみにおける糸状菌長fv だけでは、ばらつきが
出るので、複数の画面について糸状菌長fv を演算し、
その平均値で評価する。加算回路49は(7)式に従い
N画面の平均値▲▼を求める。 Since only the filamentous fungal length f v on one screen causes variations, the filamentous fungal length f v is calculated for a plurality of screens,
The average value is used for evaluation. The adder circuit 49 calculates the average value ▲ ▼ of N screens according to the equation (7).
糸状菌長平均値▲▼は糸状菌の一画面当りの画素数
を表わしている。画面の等分割で形成される各々の画素
は一定の長さを持つことから(7)式で得た画素数を用
いてサンプルの糸状菌長Lを求めることができる。 The filamentous fungus length average value ▲ ▼ represents the number of pixels per screen of the filamentous fungus. Since each pixel formed by equal division of the screen has a fixed length, the filamentous fungus length L of the sample can be obtained by using the number of pixels obtained by the equation (7).
L=l・▲▼ (8) ただし、lは画素長さ加算回路414は糸状菌の総長を
求める。L = l∇ (8) where l is the pixel length adding circuit 414, which determines the total length of the filamentous fungus.
一方、フロツクすなわちズーグレア微生物が存在する画
素Zijの2値化抽出は次式に従い、2値化回路410に
よつて求められる。On the other hand, the binarization extraction of the pixel Z ij in which the block, that is, the Zugrea microorganism is present, is obtained by the binarization circuit 410 according to the following equation.
Cij≦SlのときZij=1 …(8) Cij>SlのときZij=0 …(9) 加算回路411でZijが“1”レベルの画素をi行,j
列の各々で繰返し行い、1画面全体について加算するこ
とで、フロツク部の面積Zv を(10)式に従い求める。When C ij ≦ S l Z ij = 1 (8) When C ij > S l Z ij = 0 (9) In the adder circuit 411, the pixel whose Z ij is “1” level is i row, j
The area Z v of the floating portion is obtained according to the equation (10) by repeating the process for each of the columns and adding for the entire one screen.
さらにN画面のフロツク部画素数の平均値▲▼は
(11)式に従い、加算回路412で求める。 Further, the average value ▲ ▼ of the number of pixels in the floating portion of the N screen is calculated by the adding circuit 412 according to the equation (11).
積算回路413は(11)式で得た画素数に、画素面積を
積算する回路であり、これによりサンプルのスーグレア
性微生物面積Aを求めることができる。 The integrating circuit 413 is a circuit that integrates the pixel area with the number of pixels obtained by the equation (11), and by doing so, the area A of the Suglerous microorganism of the sample can be obtained.
A=a・▲▼ …(12) ここでaは画素面積である。A = a ..... (12) where a is the pixel area.
表示制御回路415は、原画像、fij,fv,▲
▼,L,Zij,Zv,▲▼,Aの各信号から必要な
信号のみを選択する回路である。表示制御回路415で
選択された画像はD/A変換器416でアナログ信号に
変換されモニタ15に送られ表示される。The display control circuit 415 uses the original image, f ij , f v , ▲.
This is a circuit that selects only the necessary signal from the signals ▼, L, Z ij , Z v , ▲ ▼, and A. The image selected by the display control circuit 415 is converted into an analog signal by the D / A converter 416 and sent to the monitor 15 for display.
以上のように本発明は、微生物画像を輝度情報に変換
し、その輝度情報より微生物を検出する場合、微生物の
存在しない背景輝度情報を微生物輝度情報から差引くよ
うにしているので処理を行うことで拡大光学装置やスラ
イドグラスなど検出部に生ずる光ムラ,汚れ,傷などの
諸障害を除去することができ、糸状菌がズーグレア性微
生物の量を高精度で測定することが可能になる。As described above, the present invention converts a microbial image into luminance information, and when detecting a microorganism from the luminance information, the background luminance information in which no microorganism is present is subtracted from the microorganism luminance information, so processing is performed. It is possible to remove various obstacles such as light unevenness, dirt, and scratches that occur in the detection part such as a magnifying optical device and a slide glass, and it becomes possible for the filamentous fungus to measure the amount of zooglare microorganisms with high accuracy.
本発明によれば、顕微鏡のような像拡大光学装置により
拡大された微生物相を簡単な処理で自動的に解析でき、
特に透明度の高い糸状菌とズークレア性微生物の検出に
有効である。従来用いられていた画像処理法に比べ信頼
性が高く、微生物検出部の洗浄などのメンテナンスの回
数も大幅に低減できる。According to the present invention, the microflora magnified by an image magnifying optical device such as a microscope can be automatically analyzed by a simple process,
It is particularly effective for detecting highly transparent filamentous fungi and zookerea microorganisms. The reliability is higher than that of the image processing method used conventionally, and the number of maintenance such as cleaning of the microorganism detecting portion can be greatly reduced.
なお、以上の実施例は下水処理プロセスに適用した例を
説明したが、本発明は糸状性微生物を培養する他のバイ
オプロセスにおける糸状性微生物濃度の測定にも応用で
きるのは勿論である。In addition, although the above-mentioned example explained the example applied to the sewage treatment process, it goes without saying that the present invention can be applied to the measurement of the concentration of filamentous microorganisms in other bioprocesses for culturing filamentous microorganisms.
第1図は本発明の一実施例を示す構成図、第2図は実施
例の詳細図、第3図は微生物画像の一例図、第4図は背
景の画像の一例図、第5図は微生物画像の輝度分布図、
第6図は背景画像の輝度分布図、第7図は微生物画像の
一例図、第8図は微生物画像輝度分布図、第9図は糸状
菌2値化例、第10図は糸状菌画像。 1……スライドグラス、2……像拡大光学装置、3……
撮像装置、4……情報処理装置、5……モニタ、103
……ポンプ。FIG. 1 is a block diagram showing an embodiment of the present invention, FIG. 2 is a detailed view of the embodiment, FIG. 3 is an example view of a microorganism image, FIG. 4 is an example view of a background image, and FIG. Luminance distribution map of microorganism image,
FIG. 6 is a luminance distribution diagram of a background image, FIG. 7 is an example diagram of a microorganism image, FIG. 8 is a microorganism image luminance distribution diagram, FIG. 9 is a binary example of filamentous fungi, and FIG. 10 is a filamentous fungus image. 1 …… Slide glass, 2 …… Image magnifying optical device, 3 ……
Imaging device, 4 ... Information processing device, 5 ... Monitor, 103
……pump.
───────────────────────────────────────────────────── フロントページの続き (72)発明者 依田 幹雄 茨城県日立市大みか町5丁目2番1号 株 式会社日立製作所大みか工場内 (72)発明者 原 直樹 茨城県日立市大みか町5丁目2番1号 株 式会社日立製作所大みか工場内 (56)参考文献 特開 昭60−31889(JP,A) 特開 昭61−33296(JP,A) 特開 昭61−35798(JP,A) 特開 昭60−30675(JP,A) ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Mikio Yoda 5-2-1 Omika-cho, Hitachi City, Ibaraki Prefecture Hitachi Ltd. Omika Plant, Hitachi Ltd. (72) Naoki Hara 5-chome, Omika-cho, Hitachi City, Ibaraki Prefecture No. 1 Inside the Omika Plant of Hitachi, Ltd. (56) References JP-A-60-31889 (JP, A) JP-A-61-33296 (JP, A) JP-A-61-35798 (JP, A) Special Kai 60-30675 (JP, A)
Claims (1)
と、該撮像装置によつて輝度情報に変換された微生物輝
度情報を記憶する第1記憶手段と、微生物の存在しない
背景輝度情報を記憶する第2記憶手段と、微生物輝度情
報から背景輝度情報を差引く演算手段と、背景輝度情報
を差引いた微生物輝度情報から微生物量を検出する微生
物検出手段を具備することを特徴とする微生物検出装
置。1. An image pickup device for converting a microbial image into luminance information, a first storage means for storing the microbial luminance information converted into the luminance information by the image pickup device, and background luminance information in which no microorganism exists. And a second detecting means, a calculating means for subtracting the background luminance information from the microorganism luminance information, and a microorganism detecting means for detecting the amount of microorganisms from the microorganism luminance information from which the background luminance information is subtracted. .
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP18960385A JPH0649195B2 (en) | 1985-08-30 | 1985-08-30 | Microorganism detection device |
US06/900,420 US4769776A (en) | 1985-08-30 | 1986-08-26 | Apparatus for measuring the concentration of filamentous microorganisms in a mixture including microorganisms |
KR1019860007273A KR910005632B1 (en) | 1985-08-30 | 1986-08-30 | Apparatus for measuring the concentration of filamentous microorganisms in a mixture including microorganisms |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP18960385A JPH0649195B2 (en) | 1985-08-30 | 1985-08-30 | Microorganism detection device |
Publications (2)
Publication Number | Publication Date |
---|---|
JPS6253791A JPS6253791A (en) | 1987-03-09 |
JPH0649195B2 true JPH0649195B2 (en) | 1994-06-29 |
Family
ID=16244073
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP18960385A Expired - Lifetime JPH0649195B2 (en) | 1985-08-30 | 1985-08-30 | Microorganism detection device |
Country Status (1)
Country | Link |
---|---|
JP (1) | JPH0649195B2 (en) |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0782008B2 (en) * | 1987-07-27 | 1995-09-06 | 株式会社日立製作所 | Device for diagnosing cell number and activity |
JP2001307066A (en) * | 2000-04-21 | 2001-11-02 | Matsushita Electric Ind Co Ltd | Cell image analyzer and cell image analyzing method |
JP2003139678A (en) * | 2001-10-30 | 2003-05-14 | Matsushita Electric Ind Co Ltd | Cell image analyzer, cell image analyzing method and recording medium |
JP2008545959A (en) * | 2005-05-25 | 2008-12-18 | スティフテルセン ウニヴェルジテーツフォルスクニング ベルゲン | MICROSCOPE DEVICE AND SCREENING METHOD FOR PHARMACEUTICAL, PHYSOTHERAPY AND BIOLOGICAL HAZARDOUS MATERIALS |
JP4907146B2 (en) * | 2005-10-19 | 2012-03-28 | 株式会社カネカ | Automatic culture method and cell culture apparatus |
JP6318755B2 (en) * | 2014-03-24 | 2018-05-09 | 東レ株式会社 | Filamentous bacteria detection apparatus and filamentous bacteria detection method |
CN106873411B (en) * | 2017-01-11 | 2019-02-12 | 辽宁工程技术大学 | The automatic learning control system of stalk extruding machine |
CN110118771A (en) * | 2019-03-29 | 2019-08-13 | 亳州学院 | A kind of intelligent microscopic examination device and microscopy method for active sludge microorganism |
-
1985
- 1985-08-30 JP JP18960385A patent/JPH0649195B2/en not_active Expired - Lifetime
Also Published As
Publication number | Publication date |
---|---|
JPS6253791A (en) | 1987-03-09 |
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